diff --git a/index.html b/index.html index 4e700be49bf7984cedd9a4c781e544161d11c29e..c5883e80a1d0ac233b97cf09f24ee2bc4b5294eb 100644 --- a/index.html +++ b/index.html @@ -111,18 +111,18 @@
Locality and weight sharing, the convolution operation, and CNNs in PyTorch
Open slides →
- +
Week 6
Recurrent Neural Networks
Sequence data, the recurrent layer, LSTM and GRU, and an RNN in PyTorch
-
Open slides →
- - +
Class slides →Full slides, for self-study →
+
+
Week 7
Transformers
Query, key and value; the original architecture, and what modern transformers changed
-
Open slides →
- +
Class slides →Full slides, for self-study →
+
Week 9
Generative Models and GANs
@@ -138,7 +138,7 @@ diff --git a/lecture-01/404.html b/lecture-01/404.html index 9d743bceff53ab018f5284480b70c3ff31a0aa47..6a2e015c6de187cc78dc58eaaf33a97ff39df682 100644 --- a/lecture-01/404.html +++ b/lecture-01/404.html @@ -21,17 +21,17 @@ - + - + - +
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\ No newline at end of file +import{B as e,C as t,D as n,_ as r,et as i,v as a,xt as o,yt as s}from"./modules/shiki-CJZsCeMT.js";import{n as c,t as l}from"./slidev/context-C6dxaFaS.js";import{t as u}from"./slidev/VClicks-DQvnzthI.js";import{t as d}from"./default-CBZusq11.js";var f={class:`grid grid-cols-2 gap-x-10 mt-4`},p={__name:`slides.md__slidev_6`,setup(p){let{$slidev:m,$nav:h,$clicksContext:g,$clicks:_,$page:v,$renderContext:y,$frontmatter:b}=c();return g.setup(),(c,p)=>{let m=u;return e(),a(d,o(n(s(l)(s(b),5))),{default:i(()=>[p[2]||=r(`h1`,null,`Outline`,-1),r(`div`,f,[r(`div`,null,[t(m,null,{default:i(()=>[...p[0]||=[r(`ul`,null,[r(`li`,null,`Introduction to course content`),r(`li`,null,`Course evaluation method, and exams`),r(`li`,null,`What is deep learning?`),r(`li`,null,`Application of deep learning`)],-1)]]),_:1})]),r(`div`,null,[t(m,null,{default:i(()=>[...p[1]||=[r(`ul`,null,[r(`li`,null,`Deep learning frameworks`),r(`li`,null,`Installing a deep learning framework`),r(`li`,null,`Google Colab and Kaggle`),r(`li`,null,`Jupyter notebooks`)],-1)]]),_:1})])])]),_:1},16)}}};export{p as default}; 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b}from"../modules/shiki-CJZsCeMT.js";import{h as x,u as S}from"../modules/vue-ClnAKIA7.js";import{t as C}from"../useDrawings-BUk51X_i.js";import{t as w}from"./IconButton-BwA4p7Ax.js";import{t as T}from"./VerticalDivider-Bvc1MZzH.js";var E=o({__name:`Draggable`,props:{storageKey:{},initial:{}},setup(e){let n=e,r=d(null),i=n.initial??{x:0,y:0},o=n.storageKey?x(n.storageKey,i):d(i),{style:c}=S(r,{initialValue:o});return(e,n)=>(t(),l(`div`,{ref_key:`el`,ref:r,class:`fixed`,style:a(b(c))},[s(e.$slots,`default`)],4))}}),D={class:`flex bg-main p-2`},O={class:`inline-block w-7 text-center`},k={class:`pt-.5`},A={class:`i-carbon:pin-filled transform -rotate-45`},j={class:`i-carbon:pin`},M={class:`i-carbon:error`},N={class:`i-carbon:close-outline`},P=o({__name:`DrawingControls`,setup(e){let{brush:o,canClear:s,canRedo:x,canUndo:S,clear:P,drauu:F,drawingEnabled:I,drawingMode:L,drawingPinned:R,brushColors:z}=C(),B=d(!1);function 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w}from"./IconButton-BwA4p7Ax.js";import{t as T}from"./VerticalDivider-Bvc1MZzH.js";var E=o({__name:`Draggable`,props:{storageKey:{},initial:{}},setup(e){let n=e,r=d(null),i=n.initial??{x:0,y:0},o=n.storageKey?x(n.storageKey,i):d(i),{style:c}=S(r,{initialValue:o});return(e,n)=>(t(),l(`div`,{ref_key:`el`,ref:r,class:`fixed`,style:a(b(c))},[s(e.$slots,`default`)],4))}}),D={class:`flex bg-main p-2`},O={class:`inline-block w-7 text-center`},k={class:`pt-.5`},A={class:`i-carbon:pin-filled transform -rotate-45`},j={class:`i-carbon:pin`},M={class:`i-carbon:error`},N={class:`i-carbon:close-outline`},P=o({__name:`DrawingControls`,setup(e){let{brush:o,canClear:s,canRedo:x,canUndo:S,clear:P,drauu:F,drawingEnabled:I,drawingMode:L,drawingPinned:R,brushColors:z}=C(),B=d(!1);function V(e){e.preventDefault(),e.stopPropagation(),requestAnimationFrame(()=>{requestAnimationFrame(()=>{B.value=!0})})}function H(e){e.pointerType!==`mouse`&&V(e)}function U(){F.undo()}function W(){F.redo()}let G=`stylus`;function K(e){L.value=e,I.value=!0,e!==`eraseLine`&&(G=e)}function q(e){o.value.color=e,I.value=!0,L.value=G}return(e,d)=>b(I)||b(R)?(t(),v(E,{key:0,class:u([`flex flex-wrap text-xl p-2 gap-1 rounded-md bg-main shadow transition-opacity duration-200 z-nav border border-main`,!b(I)&&b(R)?`opacity-40 hover:opacity-90`:``]),"storage-key":`slidev-drawing-pos`,"initial-x":10,"initial-y":10},{default:f(()=>[n(w,{title:`Draw with stylus`,class:u({shallow:b(L)!==`stylus`}),onClick:d[0]||=e=>K(`stylus`)},{default:f(()=>[...d[14]||=[c(`div`,{class:`i-carbon:pen`},null,-1)]]),_:1},8,[`class`]),n(w,{title:`Draw a line`,class:u({shallow:b(L)!==`line`}),onClick:d[1]||=e=>K(`line`)},{default:f(()=>[...d[15]||=[c(`svg`,{width:`1em`,height:`1em`,class:`-mt-0.5`,preserveAspectRatio:`xMidYMid meet`,viewBox:`0 0 24 24`},[c(`path`,{d:`M21.71 3.29a1 1 0 0 0-1.42 0l-18 18a1 1 0 0 0 0 1.42a1 1 0 0 0 1.42 0l18-18a1 1 0 0 0 0-1.42z`,fill:`currentColor`})],-1)]]),_:1},8,[`class`]),n(w,{title:`Draw an 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d=r({__name:`DrawingLayer`,setup(r){let{drauu:d,drawingEnabled:f,loadCanvas:p}=u(),m=l().$scale,h=s();return n(()=>{d.mount(h.value,h.value.parentElement),i(m,e=>d.options.coordinateScale=1/e,{immediate:!0}),p()}),t(()=>{d.unmount()}),(t,n)=>(e(),a(`svg`,{ref_key:`svg`,ref:h,class:o([`w-full h-full absolute top-0`,{"pointer-events-none":!c(f),"touch-none":c(f)}])},null,2))}});export{d as default}; \ No newline at end of file +import{B as e,I as t,R as n,T as r,Z as i,b as a,bt as o,dt as s,yt as c}from"../modules/shiki-CJZsCeMT.js";import{n as l}from"./context-C6dxaFaS.js";import{t as u}from"../useDrawings-BvK1PXHw.js";var d=r({__name:`DrawingLayer`,setup(r){let{drauu:d,drawingEnabled:f,loadCanvas:p}=u(),m=l().$scale,h=s();return n(()=>{d.mount(h.value,h.value.parentElement),i(m,e=>d.options.coordinateScale=1/e,{immediate:!0}),p()}),t(()=>{d.unmount()}),(t,n)=>(e(),a(`svg`,{ref_key:`svg`,ref:h,class:o([`w-full h-full absolute top-0`,{"pointer-events-none":!c(f),"touch-none":c(f)}])},null,2))}});export{d as default}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/DrawingLayer-ByCuxIAu.js b/lecture-01/assets/slidev/DrawingLayer-ByCuxIAu.js deleted file mode 100644 index 55ae4bfe570353896a4fa7c2b25e6c44f1191fc9..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/DrawingLayer-ByCuxIAu.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,I as t,R as n,T as r,Z as i,b as a,bt as o,dt as s,yt as c}from"../modules/shiki-CJZsCeMT.js";import{n as l}from"./context-C6dxaFaS.js";import{t as u}from"../useDrawings-DUewdMnS.js";var d=r({__name:`DrawingLayer`,setup(r){let{drauu:d,drawingEnabled:f,loadCanvas:p}=u(),m=l().$scale,h=s();return n(()=>{d.mount(h.value,h.value.parentElement),i(m,e=>d.options.coordinateScale=1/e,{immediate:!0}),p()}),t(()=>{d.unmount()}),(t,n)=>(e(),a(`svg`,{ref_key:`svg`,ref:h,class:o([`w-full h-full absolute top-0`,{"pointer-events-none":!c(f),"touch-none":c(f)}])},null,2))}});export{d as default}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/DrawingLayer-DTfATphV.js b/lecture-01/assets/slidev/DrawingLayer-DTfATphV.js deleted file mode 100644 index c6afbcfde92be2a8cf3d255673d13b0036a4f438..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/DrawingLayer-DTfATphV.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,I as t,R as n,T as r,Z as i,b as a,bt as o,dt as s,yt as c}from"../modules/shiki-CJZsCeMT.js";import{n as l}from"./context-C6dxaFaS.js";import{t as u}from"../useDrawings-BUk51X_i.js";var d=r({__name:`DrawingLayer`,setup(r){let{drauu:d,drawingEnabled:f,loadCanvas:p}=u(),m=l().$scale,h=s();return n(()=>{d.mount(h.value,h.value.parentElement),i(m,e=>d.options.coordinateScale=1/e,{immediate:!0}),p()}),t(()=>{d.unmount()}),(t,n)=>(e(),a(`svg`,{ref_key:`svg`,ref:h,class:o([`w-full h-full absolute top-0`,{"pointer-events-none":!c(f),"touch-none":c(f)}])},null,2))}});export{d as default}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/DrawingLayer-SxxSVnnc.js b/lecture-01/assets/slidev/DrawingLayer-SxxSVnnc.js deleted file mode 100644 index 5afa930c0620e2e874c78d5332d139c1d5d79ebf..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/DrawingLayer-SxxSVnnc.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,I as t,R as n,T as r,Z as i,b as a,bt as o,dt as s,yt as c}from"../modules/shiki-CJZsCeMT.js";import{n as l}from"./context-C6dxaFaS.js";import{t as u}from"../useDrawings-DXjUMqLI.js";var d=r({__name:`DrawingLayer`,setup(r){let{drauu:d,drawingEnabled:f,loadCanvas:p}=u(),m=l().$scale,h=s();return n(()=>{d.mount(h.value,h.value.parentElement),i(m,e=>d.options.coordinateScale=1/e,{immediate:!0}),p()}),t(()=>{d.unmount()}),(t,n)=>(e(),a(`svg`,{ref_key:`svg`,ref:h,class:o([`w-full h-full absolute 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m}from"../modules/shiki-CJZsCeMT.js";import{O as h,S as g,o as _}from"../modules/vue-ClnAKIA7.js";import{t as v}from"../index-CzJwomzX.js";import{t as y}from"./NoteDisplay-CtSe7vMG.js";var b=[`placeholder`],x=i({__name:`NoteEditable`,props:{no:{type:Number,required:!0},class:{default:``},editing:{default:!1},style:{default:()=>({})},placeholder:{default:`No notes for this slide`},clicksContext:{type:Object},highlight:{default:!0},autoHeight:{default:!1}},emits:[`update:editing`,`markerDblclick`,`markerClick`],setup(i,{emit:x}){let S=i,C=x,w=g(S,`editing`,C,{passive:!0}),{info:T,update:E}=v(u(S,`no`)),D=l(``),O,{ignoreUpdates:k}=h(D,e=>{if(!w.value)return;let t=S.no;clearTimeout(O),O=setTimeout(()=>{E({note:e},t)},500)});a(()=>T.value?.note,(e=``)=>{w.value||(clearTimeout(O),k(()=>{D.value=e}))},{immediate:!0,flush:`sync`});let A=l(),j=l();n(()=>{w.value&&A.value?.focus()}),_(A,()=>{w.value=!1});function 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p,yt as m}from"../modules/shiki-CJZsCeMT.js";import{O as h,S as g,o as _}from"../modules/vue-ClnAKIA7.js";import{t as v}from"../index-CXT8JKvy.js";import{t as y}from"./NoteDisplay-D_S7U4vx.js";var b=[`placeholder`],x=i({__name:`NoteEditable`,props:{no:{type:Number,required:!0},class:{default:``},editing:{default:!1},style:{default:()=>({})},placeholder:{default:`No notes for this slide`},clicksContext:{type:Object},highlight:{default:!0},autoHeight:{default:!1}},emits:[`update:editing`,`markerDblclick`,`markerClick`],setup(i,{emit:x}){let S=i,C=x,w=g(S,`editing`,C,{passive:!0}),{info:T,update:E}=v(u(S,`no`)),D=l(``),O,{ignoreUpdates:k}=h(D,e=>{if(!w.value)return;let t=S.no;clearTimeout(O),O=setTimeout(()=>{E({note:e},t)},500)});a(()=>T.value?.note,(e=``)=>{w.value||(clearTimeout(O),k(()=>{D.value=e}))},{immediate:!0,flush:`sync`});let A=l(),j=l();n(()=>{w.value&&A.value?.focus()}),_(A,()=>{w.value=!1});function 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ad8f7b1976628381ce7f982129856e7076b3e406..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/NoteEditable-DLqOpuTR.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,P as t,Q as n,St as r,T as i,Z as a,b as o,bt as s,d as c,dt as l,gt as u,l as d,tt as f,v as p,yt as m}from"../modules/shiki-CJZsCeMT.js";import{O as h,S as g,o as _}from"../modules/vue-ClnAKIA7.js";import{t as v}from"../index-n1yr0O7U.js";import{t as y}from"./NoteDisplay-CJVLSZOb.js";var b=[`placeholder`],x=i({__name:`NoteEditable`,props:{no:{type:Number,required:!0},class:{default:``},editing:{default:!1},style:{default:()=>({})},placeholder:{default:`No notes for this slide`},clicksContext:{type:Object},highlight:{default:!0},autoHeight:{default:!1}},emits:[`update:editing`,`markerDblclick`,`markerClick`],setup(i,{emit:x}){let S=i,C=x,w=g(S,`editing`,C,{passive:!0}),{info:T,update:E}=v(u(S,`no`)),D=l(``),O,{ignoreUpdates:k}=h(D,e=>{if(!w.value)return;let 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+++ /dev/null @@ -1 +0,0 @@ -import{B as e,P as t,Q as n,St as r,T as i,Z as a,b as o,bt as s,d as c,dt as l,gt as u,l as d,tt as f,v as p,yt as m}from"../modules/shiki-CJZsCeMT.js";import{O as h,S as g,o as _}from"../modules/vue-ClnAKIA7.js";import{t as v}from"../index-y1O4_MBz.js";import{t as y}from"./NoteDisplay-hb1xN0FA.js";var b=[`placeholder`],x=i({__name:`NoteEditable`,props:{no:{type:Number,required:!0},class:{default:``},editing:{default:!1},style:{default:()=>({})},placeholder:{default:`No notes for this slide`},clicksContext:{type:Object},highlight:{default:!0},autoHeight:{default:!1}},emits:[`update:editing`,`markerDblclick`,`markerClick`],setup(i,{emit:x}){let S=i,C=x,w=g(S,`editing`,C,{passive:!0}),{info:T,update:E}=v(u(S,`no`)),D=l(``),O,{ignoreUpdates:k}=h(D,e=>{if(!w.value)return;let t=S.no;clearTimeout(O),O=setTimeout(()=>{E({note:e},t)},500)});a(()=>T.value?.note,(e=``)=>{w.value||(clearTimeout(O),k(()=>{D.value=e}))},{immediate:!0,flush:`sync`});let 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y}from"./NoteDisplay-DSHanyu-.js";var b=[`placeholder`],x=i({__name:`NoteEditable`,props:{no:{type:Number,required:!0},class:{default:``},editing:{default:!1},style:{default:()=>({})},placeholder:{default:`No notes for this slide`},clicksContext:{type:Object},highlight:{default:!0},autoHeight:{default:!1}},emits:[`update:editing`,`markerDblclick`,`markerClick`],setup(i,{emit:x}){let S=i,C=x,w=g(S,`editing`,C,{passive:!0}),{info:T,update:E}=v(u(S,`no`)),D=l(``),O,{ignoreUpdates:k}=h(D,e=>{if(!w.value)return;let t=S.no;clearTimeout(O),O=setTimeout(()=>{E({note:e},t)},500)});a(()=>T.value?.note,(e=``)=>{w.value||(clearTimeout(O),k(()=>{D.value=e}))},{immediate:!0,flush:`sync`});let A=l(),j=l();n(()=>{w.value&&A.value?.focus()}),_(A,()=>{w.value=!1});function M(){!S.autoHeight||!A.value||!w.value||A.value.scrollHeight>A.value.clientHeight&&(A.value.style.height=`${A.value.scrollHeight}px`)}function N(e){w.value&&e.metaKey&&e.key===`s`&&(e.preventDefault(),E({note:D.value},S.no))}return 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of file diff --git a/lecture-01/assets/slidev/TimerBar-BEgeEqoH.js b/lecture-01/assets/slidev/TimerBar-BEgeEqoH.js deleted file mode 100644 index d3ae565ae325a776556ebd84a7db5a8cd6750dec..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/TimerBar-BEgeEqoH.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,C as t,H as n,St as r,T as i,a,b as o,bt as s,et as c,g as l,gt as u,lt as d,m as f,y as p,yt as m}from"../modules/shiki-CJZsCeMT.js";import{$ as h,H as g,t as _}from"../useNav-__VNesFJ.js";import{P as v}from"../modules/vue-ClnAKIA7.js";import{o as y}from"../index-y1O4_MBz.js";var b={class:`relative flex gap-px`},x=i({__name:`CurrentProgressBar`,props:{clicksContext:{},current:{}},setup(i){let u=i,d=_(),h=l(()=>u.clicksContext??d.clicksContext.value),g=l(()=>u.current??d.currentSlideNo.value),{total:v}=d;return(i,l)=>(e(),o(`div`,b,[(e(!0),o(f,null,n(m(v)-1,n=>(e(),o(`div`,{key:n,class:s([`border-x border-b border-main h-4px 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s=Math.floor(n/1e3%60).toString().padStart(2,`0`),c=Math.floor(n%1e3/100).toString();return{h:i,m:o,s,ms:c}});function d(){n.pause(),r.value={status:`stopped`,slides:{},startedAt:0,pausedAt:0}}function f(){r.value&&(r.value?.status===`stopped`?(r.value.status=`running`,r.value.startedAt=Date.now()):r.value.status===`paused`&&(r.value.status=`running`,r.value.startedAt=Date.now()-(r.value.pausedAt-r.value.startedAt)),n.resume())}function p(){r.value.status=`paused`,r.value.pausedAt=Date.now(),n.pause()}function m(){r.value.status===`running`?p():f()}return{state:r,status:i,timer:c,reset:d,toggle:m,resume:f,pause:p,passed:o,percentage:s,duration:t,mode:e}}var C={class:`border-b mt-px border-main relative flex h-4px`},w=i({__name:`TimerBar`,setup(t){let n=d(S()),i=l(()=>n.status===`stopped`?`op50`:n.status===`paused`?`bg-blue`:n.percentage>100?`bg-red`:n.percentage>80?`bg-yellow`:`bg-green`);return(t,a)=>(e(),o(`div`,C,[n.status===`stopped`?p(`v-if`,!0):(e(),o(`div`,{key:0,class:s([`h-4px`,i.value]),style:r({width:`${n.percentage}%`})},null,6)),p(` {{ timesplits }} `)]))}});export{S as n,x as r,w as t}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/TimerBar-Beg0X5gj.js b/lecture-01/assets/slidev/TimerBar-Beg0X5gj.js deleted file mode 100644 index 4d074fedc07e006b5c3d66b58af793945308cc05..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/TimerBar-Beg0X5gj.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,C as t,H as n,St as r,T as i,a,b as o,bt as s,et as c,g as l,gt as u,lt as d,m as f,y as p,yt as m}from"../modules/shiki-CJZsCeMT.js";import{$ as h,H as g,t as _}from"../useNav-By_bInSV.js";import{P as v}from"../modules/vue-ClnAKIA7.js";import{o as y}from"../index-n1yr0O7U.js";var 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e=l(()=>h.timer||`stopwatch`),t=l(()=>g(h.duration).seconds),n=v(100,{controls:!0}),r=u(y,`timer`),i=l(()=>r.value?.status),a=l(()=>(n.counter.value,r.value.status===`stopped`||!r.value.startedAt?0:r.value.status===`paused`?r.value.pausedAt-r.value.startedAt:Date.now()-r.value.startedAt)),o=l(()=>a.value/1e3),s=l(()=>o.value/t.value*100),c=l(()=>{if(e.value===`stopwatch`&&(r.value.status===`stopped`||!r.value.startedAt))return{h:``,m:`-`,s:`--`,ms:`-`};let n=e.value===`countdown`?t.value*1e3-a.value:a.value,i=Math.floor(n/1e3/60/60).toString();i===`0`&&(i=``);let o=Math.floor(n/1e3/60%60).toString();i&&(o=o.padStart(2,`0`));let s=Math.floor(n/1e3%60).toString().padStart(2,`0`),c=Math.floor(n%1e3/100).toString();return{h:i,m:o,s,ms:c}});function d(){n.pause(),r.value={status:`stopped`,slides:{},startedAt:0,pausedAt:0}}function f(){r.value&&(r.value?.status===`stopped`?(r.value.status=`running`,r.value.startedAt=Date.now()):r.value.status===`paused`&&(r.value.status=`running`,r.value.startedAt=Date.now()-(r.value.pausedAt-r.value.startedAt)),n.resume())}function p(){r.value.status=`paused`,r.value.pausedAt=Date.now(),n.pause()}function m(){r.value.status===`running`?p():f()}return{state:r,status:i,timer:c,reset:d,toggle:m,resume:f,pause:p,passed:o,percentage:s,duration:t,mode:e}}var C={class:`border-b mt-px border-main relative flex h-4px`},w=i({__name:`TimerBar`,setup(t){let n=d(S()),i=l(()=>n.status===`stopped`?`op50`:n.status===`paused`?`bg-blue`:n.percentage>100?`bg-red`:n.percentage>80?`bg-yellow`:`bg-green`);return(t,a)=>(e(),o(`div`,C,[n.status===`stopped`?p(`v-if`,!0):(e(),o(`div`,{key:0,class:s([`h-4px`,i.value]),style:r({width:`${n.percentage}%`})},null,6)),p(` {{ timesplits }} `)]))}});export{S as n,x as r,w as t}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/TimerBar-D_lz8GJ6.js b/lecture-01/assets/slidev/TimerBar-DB-yAKCI.js similarity index 95% rename from lecture-01/assets/slidev/TimerBar-D_lz8GJ6.js rename to lecture-01/assets/slidev/TimerBar-DB-yAKCI.js index f5bb18040e0fc5ba18b10044ad938c50e642597e..6e2bd59917262b4504d5f06fca3ef8eec31116ca 100644 --- a/lecture-01/assets/slidev/TimerBar-D_lz8GJ6.js +++ b/lecture-01/assets/slidev/TimerBar-DB-yAKCI.js @@ -1 +1 @@ -import{B as e,C as t,H as n,St as r,T as i,a,b as o,bt as s,et as c,g as l,gt as u,lt as d,m as f,y as p,yt as m}from"../modules/shiki-CJZsCeMT.js";import{$ as h,H as g,t as _}from"../useNav-DmKDaGWK.js";import{P as v}from"../modules/vue-ClnAKIA7.js";import{o as y}from"../index-CzJwomzX.js";var b={class:`relative flex gap-px`},x=i({__name:`CurrentProgressBar`,props:{clicksContext:{},current:{}},setup(i){let 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n=e.value===`countdown`?t.value*1e3-a.value:a.value,i=Math.floor(n/1e3/60/60).toString();i===`0`&&(i=``);let o=Math.floor(n/1e3/60%60).toString();i&&(o=o.padStart(2,`0`));let s=Math.floor(n/1e3%60).toString().padStart(2,`0`),c=Math.floor(n%1e3/100).toString();return{h:i,m:o,s,ms:c}});function d(){n.pause(),r.value={status:`stopped`,slides:{},startedAt:0,pausedAt:0}}function f(){r.value&&(r.value?.status===`stopped`?(r.value.status=`running`,r.value.startedAt=Date.now()):r.value.status===`paused`&&(r.value.status=`running`,r.value.startedAt=Date.now()-(r.value.pausedAt-r.value.startedAt)),n.resume())}function p(){r.value.status=`paused`,r.value.pausedAt=Date.now(),n.pause()}function m(){r.value.status===`running`?p():f()}return{state:r,status:i,timer:c,reset:d,toggle:m,resume:f,pause:p,passed:o,percentage:s,duration:t,mode:e}}var C={class:`border-b mt-px border-main relative flex h-4px`},w=i({__name:`TimerBar`,setup(t){let n=d(S()),i=l(()=>n.status===`stopped`?`op50`:n.status===`paused`?`bg-blue`:n.percentage>100?`bg-red`:n.percentage>80?`bg-yellow`:`bg-green`);return(t,a)=>(e(),o(`div`,C,[n.status===`stopped`?p(`v-if`,!0):(e(),o(`div`,{key:0,class:s([`h-4px`,i.value]),style:r({width:`${n.percentage}%`})},null,6)),p(` {{ timesplits }} `)]))}});export{S as n,x as r,w as t}; \ No newline at end of file +import{B as e,C as t,H as n,St as r,T as i,a,b as o,bt as s,et as c,g as l,gt as u,lt as d,m as f,y as p,yt as m}from"../modules/shiki-CJZsCeMT.js";import{$ as h,H as g,t as _}from"../useNav-DdRarJm8.js";import{P as v}from"../modules/vue-ClnAKIA7.js";import{o as y}from"../index-Bm7ETj3d.js";var b={class:`relative flex gap-px`},x=i({__name:`CurrentProgressBar`,props:{clicksContext:{},current:{}},setup(i){let u=i,d=_(),h=l(()=>u.clicksContext??d.clicksContext.value),g=l(()=>u.current??d.currentSlideNo.value),{total:v}=d;return(i,l)=>(e(),o(`div`,b,[(e(!0),o(f,null,n(m(v)-1,n=>(e(),o(`div`,{key:n,class:s([`border-x border-b border-main h-4px transition-all`,n[n===g.value?(e(),o(`div`,{key:0,class:`h-full bg-primary op75 transition-all`,style:r({width:`${h.value.total===0?0:h.value.current/(h.value.total+1)*100}%`})},null,4)):p(`v-if`,!0)]),_:2},1024)],6))),128))]))}});function S(){let e=l(()=>h.timer||`stopwatch`),t=l(()=>g(h.duration).seconds),n=v(100,{controls:!0}),r=u(y,`timer`),i=l(()=>r.value?.status),a=l(()=>(n.counter.value,r.value.status===`stopped`||!r.value.startedAt?0:r.value.status===`paused`?r.value.pausedAt-r.value.startedAt:Date.now()-r.value.startedAt)),o=l(()=>a.value/1e3),s=l(()=>o.value/t.value*100),c=l(()=>{if(e.value===`stopwatch`&&(r.value.status===`stopped`||!r.value.startedAt))return{h:``,m:`-`,s:`--`,ms:`-`};let n=e.value===`countdown`?t.value*1e3-a.value:a.value,i=Math.floor(n/1e3/60/60).toString();i===`0`&&(i=``);let o=Math.floor(n/1e3/60%60).toString();i&&(o=o.padStart(2,`0`));let s=Math.floor(n/1e3%60).toString().padStart(2,`0`),c=Math.floor(n%1e3/100).toString();return{h:i,m:o,s,ms:c}});function d(){n.pause(),r.value={status:`stopped`,slides:{},startedAt:0,pausedAt:0}}function f(){r.value&&(r.value?.status===`stopped`?(r.value.status=`running`,r.value.startedAt=Date.now()):r.value.status===`paused`&&(r.value.status=`running`,r.value.startedAt=Date.now()-(r.value.pausedAt-r.value.startedAt)),n.resume())}function p(){r.value.status=`paused`,r.value.pausedAt=Date.now(),n.pause()}function m(){r.value.status===`running`?p():f()}return{state:r,status:i,timer:c,reset:d,toggle:m,resume:f,pause:p,passed:o,percentage:s,duration:t,mode:e}}var C={class:`border-b mt-px border-main relative flex h-4px`},w=i({__name:`TimerBar`,setup(t){let n=d(S()),i=l(()=>n.status===`stopped`?`op50`:n.status===`paused`?`bg-blue`:n.percentage>100?`bg-red`:n.percentage>80?`bg-yellow`:`bg-green`);return(t,a)=>(e(),o(`div`,C,[n.status===`stopped`?p(`v-if`,!0):(e(),o(`div`,{key:0,class:s([`h-4px`,i.value]),style:r({width:`${n.percentage}%`})},null,6)),p(` {{ timesplits }} `)]))}});export{S as n,x as r,w as t}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/TimerBar-DpsKy8bj.js b/lecture-01/assets/slidev/TimerBar-DpsKy8bj.js deleted file mode 100644 index 73247f6bd70d290d343840a32afd3b9c33e0f0d3..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/TimerBar-DpsKy8bj.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,C as t,H as n,St as r,T as i,a,b as o,bt as s,et as c,g as l,gt as u,lt as d,m as f,y as p,yt as m}from"../modules/shiki-CJZsCeMT.js";import{$ as h,H as g,t as _}from"../useNav-CCJY516K.js";import{P as v}from"../modules/vue-ClnAKIA7.js";import{o as y}from"../index-CXT8JKvy.js";var b={class:`relative flex gap-px`},x=i({__name:`CurrentProgressBar`,props:{clicksContext:{},current:{}},setup(i){let u=i,d=_(),h=l(()=>u.clicksContext??d.clicksContext.value),g=l(()=>u.current??d.currentSlideNo.value),{total:v}=d;return(i,l)=>(e(),o(`div`,b,[(e(!0),o(f,null,n(m(v)-1,n=>(e(),o(`div`,{key:n,class:s([`border-x border-b border-main h-4px transition-all`,n[n===g.value?(e(),o(`div`,{key:0,class:`h-full bg-primary op75 transition-all`,style:r({width:`${h.value.total===0?0:h.value.current/(h.value.total+1)*100}%`})},null,4)):p(`v-if`,!0)]),_:2},1024)],6))),128))]))}});function S(){let e=l(()=>h.timer||`stopwatch`),t=l(()=>g(h.duration).seconds),n=v(100,{controls:!0}),r=u(y,`timer`),i=l(()=>r.value?.status),a=l(()=>(n.counter.value,r.value.status===`stopped`||!r.value.startedAt?0:r.value.status===`paused`?r.value.pausedAt-r.value.startedAt:Date.now()-r.value.startedAt)),o=l(()=>a.value/1e3),s=l(()=>o.value/t.value*100),c=l(()=>{if(e.value===`stopwatch`&&(r.value.status===`stopped`||!r.value.startedAt))return{h:``,m:`-`,s:`--`,ms:`-`};let n=e.value===`countdown`?t.value*1e3-a.value:a.value,i=Math.floor(n/1e3/60/60).toString();i===`0`&&(i=``);let o=Math.floor(n/1e3/60%60).toString();i&&(o=o.padStart(2,`0`));let s=Math.floor(n/1e3%60).toString().padStart(2,`0`),c=Math.floor(n%1e3/100).toString();return{h:i,m:o,s,ms:c}});function d(){n.pause(),r.value={status:`stopped`,slides:{},startedAt:0,pausedAt:0}}function f(){r.value&&(r.value?.status===`stopped`?(r.value.status=`running`,r.value.startedAt=Date.now()):r.value.status===`paused`&&(r.value.status=`running`,r.value.startedAt=Date.now()-(r.value.pausedAt-r.value.startedAt)),n.resume())}function p(){r.value.status=`paused`,r.value.pausedAt=Date.now(),n.pause()}function m(){r.value.status===`running`?p():f()}return{state:r,status:i,timer:c,reset:d,toggle:m,resume:f,pause:p,passed:o,percentage:s,duration:t,mode:e}}var C={class:`border-b mt-px border-main relative flex h-4px`},w=i({__name:`TimerBar`,setup(t){let n=d(S()),i=l(()=>n.status===`stopped`?`op50`:n.status===`paused`?`bg-blue`:n.percentage>100?`bg-red`:n.percentage>80?`bg-yellow`:`bg-green`);return(t,a)=>(e(),o(`div`,C,[n.status===`stopped`?p(`v-if`,!0):(e(),o(`div`,{key:0,class:s([`h-4px`,i.value]),style:r({width:`${n.percentage}%`})},null,6)),p(` {{ timesplits }} `)]))}});export{S as n,x as r,w as t}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/TimerBar-Dqv959vf.js b/lecture-01/assets/slidev/TimerBar-Dqv959vf.js deleted file mode 100644 index 7e4de3d01c4ecc0f58d9e2cf22141b03a15c468d..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/TimerBar-Dqv959vf.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,C as t,H as n,St as r,T as i,a,b as o,bt as s,et as c,g as l,gt as u,lt as d,m as f,y as p,yt as m}from"../modules/shiki-CJZsCeMT.js";import{$ as h,H as g,t as _}from"../useNav-4_ipXRLC.js";import{P as v}from"../modules/vue-ClnAKIA7.js";import{o as y}from"../index-C8LcJbwp.js";var b={class:`relative flex gap-px`},x=i({__name:`CurrentProgressBar`,props:{clicksContext:{},current:{}},setup(i){let u=i,d=_(),h=l(()=>u.clicksContext??d.clicksContext.value),g=l(()=>u.current??d.currentSlideNo.value),{total:v}=d;return(i,l)=>(e(),o(`div`,b,[(e(!0),o(f,null,n(m(v)-1,n=>(e(),o(`div`,{key:n,class:s([`border-x border-b border-main h-4px 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s=Math.floor(n/1e3%60).toString().padStart(2,`0`),c=Math.floor(n%1e3/100).toString();return{h:i,m:o,s,ms:c}});function d(){n.pause(),r.value={status:`stopped`,slides:{},startedAt:0,pausedAt:0}}function f(){r.value&&(r.value?.status===`stopped`?(r.value.status=`running`,r.value.startedAt=Date.now()):r.value.status===`paused`&&(r.value.status=`running`,r.value.startedAt=Date.now()-(r.value.pausedAt-r.value.startedAt)),n.resume())}function p(){r.value.status=`paused`,r.value.pausedAt=Date.now(),n.pause()}function m(){r.value.status===`running`?p():f()}return{state:r,status:i,timer:c,reset:d,toggle:m,resume:f,pause:p,passed:o,percentage:s,duration:t,mode:e}}var C={class:`border-b mt-px border-main relative flex h-4px`},w=i({__name:`TimerBar`,setup(t){let n=d(S()),i=l(()=>n.status===`stopped`?`op50`:n.status===`paused`?`bg-blue`:n.percentage>100?`bg-red`:n.percentage>80?`bg-yellow`:`bg-green`);return(t,a)=>(e(),o(`div`,C,[n.status===`stopped`?p(`v-if`,!0):(e(),o(`div`,{key:0,class:s([`h-4px`,i.value]),style:r({width:`${n.percentage}%`})},null,6)),p(` {{ timesplits }} `)]))}});export{S as n,x as r,w as t}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/VClicks-C5fIN4Mc.js b/lecture-01/assets/slidev/VClicks-C5fIN4Mc.js deleted file mode 100644 index 8fff58bf58639892172c945755957472374a3b24..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/VClicks-C5fIN4Mc.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,C as t,G as n,O as r,R as i,T as a,j as o,m as s,p as c,tt as l,v as u,z as d}from"../modules/shiki-CJZsCeMT.js";import{G as f,it as p}from"../useNav-__VNesFJ.js";import{n as m}from"./context-C6dxaFaS.js";import{u as h}from"../index-y1O4_MBz.js";var g=a({__name:`VClickGap`,props:{size:{type:[String,Number],default:1}},setup(t){let n=t,{$clicksContext:r}=m(),a=h(),o=+n.size;return Number.isNaN(o)&&(console.warn(`[slidev] Invalid size for VClickGap: ${n.size}`),o=1),i(()=>{let e=r.currentOffset+o-1;r.register(a,{max:e,delta:o})}),d(()=>{r.unregister(a)}),(t,n)=>(e(),u(s))}}),_=[`ul`,`ol`],v=/[\s,]+/,y=a({props:{depth:{type:[Number,String],default:1},every:{type:[Number,String],default:1},at:{type:[Number,String],default:`+1`},hide:{type:Boolean,default:!1},fade:{type:Boolean,default:!1},animation:{type:[String,Array],default:void 0},handleSpecialElements:{type:Boolean,default:!0}},render(){let e=+this.every,i=f(this.at),a=typeof i==`string`,s=this.$slots.default?.();if(i==null||!s)return s;let u=n(`click`),d=(e,t)=>{let n={hide:this.hide};return typeof this.animation==`string`?this.animation.split(v).forEach(e=>{e&&(n[e]=!0)}):Array.isArray(this.animation)&&this.animation.forEach(e=>n[e]=!0),this.fade&&(n.fade=!0),l(e,[[u,t,``,n]])},m=e=>e.flatMap(e=>o(e)&&typeof e.type==`symbol`&&Array.isArray(e.children)?m(e.children):[e]);s=m(p(s));let h=(e,t=1)=>m(e).map(e=>{if(!o(e))return e;if(_.includes(e.type)&&Array.isArray(e.children)){let n=x(e.children,t+1);return r(e,{},n)}return r(e)}),y=1,b=0,x=(t,n=1)=>m(t).map(t=>{if(!o(t)||t.type===c)return t;let s=+i+Math.ceil(y++/e)-1,l;l=n<+this.depth&&Array.isArray(t.children)?r(t,{},h(t.children,n)):r(t);let u=s-b;return b=s,d(l,a?u>=0?`+${u}`:`${u}`:s)}),S=()=>t(g,{size:+i+Math.ceil((y-1)/e)-1-b});if(this.handleSpecialElements){if(s.length===1&&_.includes(s[0].type)&&Array.isArray(s[0].children))return r(s[0],{},[...x(s[0].children),S()]);if(s.length===1&&s[0].type===`table`){let e=s[0];if(Array.isArray(e.children))return 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8fc53f21586d039526ab708849039846fd99b9bf..b7937a8bc6b911153404b25ae4acb760ddf44545 100644 --- a/lecture-01/assets/slidev/VClicks-B8wlhdtp.js +++ b/lecture-01/assets/slidev/VClicks-DQvnzthI.js @@ -1 +1 @@ -import{B as e,C as t,G as n,O as r,R as i,T as a,j as o,m as s,p as c,tt as l,v as u,z as d}from"../modules/shiki-CJZsCeMT.js";import{G as f,it as p}from"../useNav-DmKDaGWK.js";import{n as m}from"./context-C6dxaFaS.js";import{u as h}from"../index-CzJwomzX.js";var g=a({__name:`VClickGap`,props:{size:{type:[String,Number],default:1}},setup(t){let n=t,{$clicksContext:r}=m(),a=h(),o=+n.size;return Number.isNaN(o)&&(console.warn(`[slidev] Invalid size for VClickGap: ${n.size}`),o=1),i(()=>{let e=r.currentOffset+o-1;r.register(a,{max:e,delta:o})}),d(()=>{r.unregister(a)}),(t,n)=>(e(),u(s))}}),_=[`ul`,`ol`],v=/[\s,]+/,y=a({props:{depth:{type:[Number,String],default:1},every:{type:[Number,String],default:1},at:{type:[Number,String],default:`+1`},hide:{type:Boolean,default:!1},fade:{type:Boolean,default:!1},animation:{type:[String,Array],default:void 0},handleSpecialElements:{type:Boolean,default:!0}},render(){let e=+this.every,i=f(this.at),a=typeof i==`string`,s=this.$slots.default?.();if(i==null||!s)return s;let u=n(`click`),d=(e,t)=>{let n={hide:this.hide};return typeof this.animation==`string`?this.animation.split(v).forEach(e=>{e&&(n[e]=!0)}):Array.isArray(this.animation)&&this.animation.forEach(e=>n[e]=!0),this.fade&&(n.fade=!0),l(e,[[u,t,``,n]])},m=e=>e.flatMap(e=>o(e)&&typeof e.type==`symbol`&&Array.isArray(e.children)?m(e.children):[e]);s=m(p(s));let h=(e,t=1)=>m(e).map(e=>{if(!o(e))return e;if(_.includes(e.type)&&Array.isArray(e.children)){let n=x(e.children,t+1);return r(e,{},n)}return r(e)}),y=1,b=0,x=(t,n=1)=>m(t).map(t=>{if(!o(t)||t.type===c)return t;let s=+i+Math.ceil(y++/e)-1,l;l=n<+this.depth&&Array.isArray(t.children)?r(t,{},h(t.children,n)):r(t);let u=s-b;return b=s,d(l,a?u>=0?`+${u}`:`${u}`:s)}),S=()=>t(g,{size:+i+Math.ceil((y-1)/e)-1-b});if(this.handleSpecialElements){if(s.length===1&&_.includes(s[0].type)&&Array.isArray(s[0].children))return r(s[0],{},[...x(s[0].children),S()]);if(s.length===1&&s[0].type===`table`){let e=s[0];if(Array.isArray(e.children))return r(e,{},e.children.map(e=>o(e)?e.type===`tbody`&&Array.isArray(e.children)?r(e,{},[...x(e.children),S()]):r(e):e))}}return[...x(s),S()]}});export{y as t}; \ No newline at end of file +import{B as e,C as t,G as n,O as r,R as i,T as a,j as o,m as s,p as c,tt as l,v as u,z as d}from"../modules/shiki-CJZsCeMT.js";import{G as f,it as p}from"../useNav-DdRarJm8.js";import{n as m}from"./context-C6dxaFaS.js";import{u as h}from"../index-Bm7ETj3d.js";var g=a({__name:`VClickGap`,props:{size:{type:[String,Number],default:1}},setup(t){let n=t,{$clicksContext:r}=m(),a=h(),o=+n.size;return Number.isNaN(o)&&(console.warn(`[slidev] Invalid size for VClickGap: ${n.size}`),o=1),i(()=>{let e=r.currentOffset+o-1;r.register(a,{max:e,delta:o})}),d(()=>{r.unregister(a)}),(t,n)=>(e(),u(s))}}),_=[`ul`,`ol`],v=/[\s,]+/,y=a({props:{depth:{type:[Number,String],default:1},every:{type:[Number,String],default:1},at:{type:[Number,String],default:`+1`},hide:{type:Boolean,default:!1},fade:{type:Boolean,default:!1},animation:{type:[String,Array],default:void 0},handleSpecialElements:{type:Boolean,default:!0}},render(){let e=+this.every,i=f(this.at),a=typeof i==`string`,s=this.$slots.default?.();if(i==null||!s)return s;let u=n(`click`),d=(e,t)=>{let n={hide:this.hide};return typeof this.animation==`string`?this.animation.split(v).forEach(e=>{e&&(n[e]=!0)}):Array.isArray(this.animation)&&this.animation.forEach(e=>n[e]=!0),this.fade&&(n.fade=!0),l(e,[[u,t,``,n]])},m=e=>e.flatMap(e=>o(e)&&typeof e.type==`symbol`&&Array.isArray(e.children)?m(e.children):[e]);s=m(p(s));let h=(e,t=1)=>m(e).map(e=>{if(!o(e))return e;if(_.includes(e.type)&&Array.isArray(e.children)){let n=x(e.children,t+1);return r(e,{},n)}return r(e)}),y=1,b=0,x=(t,n=1)=>m(t).map(t=>{if(!o(t)||t.type===c)return t;let s=+i+Math.ceil(y++/e)-1,l;l=n<+this.depth&&Array.isArray(t.children)?r(t,{},h(t.children,n)):r(t);let u=s-b;return b=s,d(l,a?u>=0?`+${u}`:`${u}`:s)}),S=()=>t(g,{size:+i+Math.ceil((y-1)/e)-1-b});if(this.handleSpecialElements){if(s.length===1&&_.includes(s[0].type)&&Array.isArray(s[0].children))return r(s[0],{},[...x(s[0].children),S()]);if(s.length===1&&s[0].type===`table`){let e=s[0];if(Array.isArray(e.children))return r(e,{},e.children.map(e=>o(e)?e.type===`tbody`&&Array.isArray(e.children)?r(e,{},[...x(e.children),S()]):r(e):e))}}return[...x(s),S()]}});export{y as t}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/VClicks-jMOOwn0i.js b/lecture-01/assets/slidev/VClicks-jMOOwn0i.js deleted file mode 100644 index ca3ee639452c8ab593339af44bad37db9fcbccca..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/VClicks-jMOOwn0i.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,C as t,G as n,O as r,R as i,T as a,j as o,m as s,p as c,tt as l,v as u,z as d}from"../modules/shiki-CJZsCeMT.js";import{G as f,it as p}from"../useNav-4_ipXRLC.js";import{n as m}from"./context-C6dxaFaS.js";import{u as h}from"../index-C8LcJbwp.js";var g=a({__name:`VClickGap`,props:{size:{type:[String,Number],default:1}},setup(t){let n=t,{$clicksContext:r}=m(),a=h(),o=+n.size;return Number.isNaN(o)&&(console.warn(`[slidev] Invalid size for VClickGap: ${n.size}`),o=1),i(()=>{let e=r.currentOffset+o-1;r.register(a,{max:e,delta:o})}),d(()=>{r.unregister(a)}),(t,n)=>(e(),u(s))}}),_=[`ul`,`ol`],v=/[\s,]+/,y=a({props:{depth:{type:[Number,String],default:1},every:{type:[Number,String],default:1},at:{type:[Number,String],default:`+1`},hide:{type:Boolean,default:!1},fade:{type:Boolean,default:!1},animation:{type:[String,Array],default:void 0},handleSpecialElements:{type:Boolean,default:!0}},render(){let e=+this.every,i=f(this.at),a=typeof i==`string`,s=this.$slots.default?.();if(i==null||!s)return s;let u=n(`click`),d=(e,t)=>{let n={hide:this.hide};return typeof this.animation==`string`?this.animation.split(v).forEach(e=>{e&&(n[e]=!0)}):Array.isArray(this.animation)&&this.animation.forEach(e=>n[e]=!0),this.fade&&(n.fade=!0),l(e,[[u,t,``,n]])},m=e=>e.flatMap(e=>o(e)&&typeof e.type==`symbol`&&Array.isArray(e.children)?m(e.children):[e]);s=m(p(s));let h=(e,t=1)=>m(e).map(e=>{if(!o(e))return e;if(_.includes(e.type)&&Array.isArray(e.children)){let 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/dev/null @@ -1 +0,0 @@ -import{B as e,C as t,G as n,O as r,R as i,T as a,j as o,m as s,p as c,tt as l,v as u,z as d}from"../modules/shiki-CJZsCeMT.js";import{G as f,it as p}from"../useNav-CCJY516K.js";import{n as m}from"./context-C6dxaFaS.js";import{u as h}from"../index-CXT8JKvy.js";var g=a({__name:`VClickGap`,props:{size:{type:[String,Number],default:1}},setup(t){let n=t,{$clicksContext:r}=m(),a=h(),o=+n.size;return Number.isNaN(o)&&(console.warn(`[slidev] Invalid size for VClickGap: ${n.size}`),o=1),i(()=>{let e=r.currentOffset+o-1;r.register(a,{max:e,delta:o})}),d(()=>{r.unregister(a)}),(t,n)=>(e(),u(s))}}),_=[`ul`,`ol`],v=/[\s,]+/,y=a({props:{depth:{type:[Number,String],default:1},every:{type:[Number,String],default:1},at:{type:[Number,String],default:`+1`},hide:{type:Boolean,default:!1},fade:{type:Boolean,default:!1},animation:{type:[String,Array],default:void 0},handleSpecialElements:{type:Boolean,default:!0}},render(){let e=+this.every,i=f(this.at),a=typeof i==`string`,s=this.$slots.default?.();if(i==null||!s)return s;let u=n(`click`),d=(e,t)=>{let n={hide:this.hide};return typeof this.animation==`string`?this.animation.split(v).forEach(e=>{e&&(n[e]=!0)}):Array.isArray(this.animation)&&this.animation.forEach(e=>n[e]=!0),this.fade&&(n.fade=!0),l(e,[[u,t,``,n]])},m=e=>e.flatMap(e=>o(e)&&typeof e.type==`symbol`&&Array.isArray(e.children)?m(e.children):[e]);s=m(p(s));let h=(e,t=1)=>m(e).map(e=>{if(!o(e))return e;if(_.includes(e.type)&&Array.isArray(e.children)){let n=x(e.children,t+1);return r(e,{},n)}return r(e)}),y=1,b=0,x=(t,n=1)=>m(t).map(t=>{if(!o(t)||t.type===c)return t;let s=+i+Math.ceil(y++/e)-1,l;l=n<+this.depth&&Array.isArray(t.children)?r(t,{},h(t.children,n)):r(t);let u=s-b;return b=s,d(l,a?u>=0?`+${u}`:`${u}`:s)}),S=()=>t(g,{size:+i+Math.ceil((y-1)/e)-1-b});if(this.handleSpecialElements){if(s.length===1&&_.includes(s[0].type)&&Array.isArray(s[0].children))return r(s[0],{},[...x(s[0].children),S()]);if(s.length===1&&s[0].type===`table`){let e=s[0];if(Array.isArray(e.children))return r(e,{},e.children.map(e=>o(e)?e.type===`tbody`&&Array.isArray(e.children)?r(e,{},[...x(e.children),S()]):r(e):e))}}return[...x(s),S()]}});export{y as t}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/client-Bm4juGWa.js b/lecture-01/assets/slidev/client-Bm4juGWa.js new file mode 100644 index 0000000000000000000000000000000000000000..73496417d57dc4214be3b7b59f02a4609ce854b5 --- /dev/null +++ b/lecture-01/assets/slidev/client-Bm4juGWa.js @@ -0,0 +1 @@ +import"../useNav-DdRarJm8.js";import"./context-C6dxaFaS.js";import"../syncState-3Tc2ZArb.js";import"../index-Bm7ETj3d.js";import"../useDrawings-BvK1PXHw.js"; \ No newline at end of file diff --git a/lecture-01/assets/slidev/notes-7UIjYIpP.js b/lecture-01/assets/slidev/notes-7UIjYIpP.js deleted file mode 100644 index 0da8e45a204429df9c709ba9a2fa8da11d49f69f..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/notes-7UIjYIpP.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,C as t,Ct as n,S as r,St as i,T as a,Z as o,_ as s,b as c,dt as l,et as u,g as d,m as f,yt as p}from"../modules/shiki-CJZsCeMT.js";import{U as m,Z as h,t as g,u as _}from"../useNav-__VNesFJ.js";import{h as v}from"../modules/vue-ClnAKIA7.js";import{m as y,o as b}from"../index-y1O4_MBz.js";import{t as x}from"./IconButton-BwA4p7Ax.js";import{t as S}from"./Modal-cvUjgiyd.js";import{n as C,t as w}from"./NoteDisplay-hb1xN0FA.js";import{r as T,t as E}from"./TimerBar-BEgeEqoH.js";var D={class:`flex my-1`},O={class:`h-full flex flex-col`},k={class:`flex-none border-t border-main`,px3:``,py2:``},A={class:`flex-none border-t border-main`},j={class:`flex gap-1 items-center px-6 py-3`},M={key:0,class:`i-carbon:minimize`},N={key:1,class:`i-carbon:maximize`},P={class:`px2 my-auto`},F={class:`text-lg`},I={class:`opacity-50 text-sm`},L=a({__name:`notes`,setup(a){y({title:`Notes - ${h}`});let{slides:L,total:R}=g(),{isFullscreen:z,toggle:B}=_,V=l(),H=v(`slidev-notes-font-size`,18),U=d(()=>b.page),W=l(!1),G=d(()=>L.value.find(e=>e.no===U.value));o(U,()=>{V.value?.scrollTo({left:0,top:0,behavior:`smooth`}),window.scrollTo({left:0,top:0,behavior:`smooth`})});function K(){H.value+=1}function q(){--H.value}let J=d(()=>{let e=b.clicks,t=b.clicksTotal;return m(l(e),void 0,t)});return(a,o)=>(e(),c(f,null,[t(S,{modelValue:W.value,"onUpdate:modelValue":o[1]||=e=>W.value=e,class:`px-6 py-4 flex flex-col gap-2`},{default:u(()=>[o[3]||=s(`div`,{class:`flex gap-2 text-xl`},[s(`div`,{class:`i-carbon:information my-auto`}),r(` Help `)],-1),o[4]||=s(`div`,{class:`prose dark:prose-invert`},[s(`p`,null,`This is the hands-free live notes viewer.`),s(`p`,null,`It's designed to be used in a separate view or device. The progress is controlled by and auto synced with the main presenter or slide.`)],-1),s(`div`,D,[s(`button`,{class:`slidev-form-button`,onClick:o[0]||=e=>W.value=!1},` Close `)])]),_:1},8,[`modelValue`]),s(`div`,O,[t(T,{"clicks-context":J.value,current:U.value},null,8,[`clicks-context`,`current`]),t(E),s(`div`,{ref_key:`scroller`,ref:V,class:`px-5 py-3 flex-auto h-full overflow-auto`,style:i({fontSize:`${p(H)}px`})},[t(w,{note:G.value?.meta.slide.note,"note-html":G.value?.meta.slide.noteHTML,placeholder:`No notes for Slide ${U.value}.`,"clicks-context":J.value,"auto-scroll":!0},null,8,[`note`,`note-html`,`placeholder`,`clicks-context`])],4),s(`div`,k,[t(C,{"clicks-context":J.value,readonly:``},null,8,[`clicks-context`])]),s(`div`,A,[s(`div`,j,[t(x,{title:p(z)?`Close fullscreen`:`Enter fullscreen`,onClick:p(B)},{default:u(()=>[p(z)?(e(),c(`div`,M)):(e(),c(`div`,N))]),_:1},8,[`title`,`onClick`]),t(x,{title:`Increase font size`,onClick:K},{default:u(()=>[...o[5]||=[s(`div`,{class:`i-carbon:zoom-in`},null,-1)]]),_:1}),t(x,{title:`Decrease font size`,onClick:q},{default:u(()=>[...o[6]||=[s(`div`,{class:`i-carbon:zoom-out`},null,-1)]]),_:1}),t(x,{title:`Edit notes`,to:`/notes-edit`,target:`_blank`},{default:u(()=>[...o[7]||=[s(`div`,{class:`i-carbon:edit`},null,-1)]]),_:1}),t(x,{title:`Help`,class:`rounded-full`,onClick:o[2]||=e=>W.value=!0},{default:u(()=>[...o[8]||=[s(`div`,{class:`i-carbon:help`},null,-1)]]),_:1}),o[9]||=s(`div`,{class:`flex-auto`},null,-1),s(`div`,P,[s(`span`,F,n(U.value),1),s(`span`,I,` / `+n(p(R)),1)])])])])],64))}});export{L as default}; \ No newline at end of file diff --git a/lecture-01/assets/slidev/notes-BHfThDko.js b/lecture-01/assets/slidev/notes-BHfThDko.js deleted file mode 100644 index e87cbeacc20e34de782ac0560403c8586decc10e..0000000000000000000000000000000000000000 --- a/lecture-01/assets/slidev/notes-BHfThDko.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,C as t,Ct as n,S as r,St as i,T as a,Z as o,_ as s,b as c,dt as l,et as u,g as d,m as f,yt as p}from"../modules/shiki-CJZsCeMT.js";import{U as m,Z as h,t as g,u as _}from"../useNav-CCJY516K.js";import{h as v}from"../modules/vue-ClnAKIA7.js";import{m as y,o as b}from"../index-CXT8JKvy.js";import{t as x}from"./IconButton-BwA4p7Ax.js";import{t as S}from"./Modal-cvUjgiyd.js";import{n as C,t as w}from"./NoteDisplay-D_S7U4vx.js";import{r as T,t as E}from"./TimerBar-DpsKy8bj.js";var D={class:`flex my-1`},O={class:`h-full flex flex-col`},k={class:`flex-none border-t border-main`,px3:``,py2:``},A={class:`flex-none border-t border-main`},j={class:`flex gap-1 items-center px-6 py-3`},M={key:0,class:`i-carbon:minimize`},N={key:1,class:`i-carbon:maximize`},P={class:`px2 my-auto`},F={class:`text-lg`},I={class:`opacity-50 text-sm`},L=a({__name:`notes`,setup(a){y({title:`Notes - ${h}`});let{slides:L,total:R}=g(),{isFullscreen:z,toggle:B}=_,V=l(),H=v(`slidev-notes-font-size`,18),U=d(()=>b.page),W=l(!1),G=d(()=>L.value.find(e=>e.no===U.value));o(U,()=>{V.value?.scrollTo({left:0,top:0,behavior:`smooth`}),window.scrollTo({left:0,top:0,behavior:`smooth`})});function K(){H.value+=1}function q(){--H.value}let J=d(()=>{let e=b.clicks,t=b.clicksTotal;return m(l(e),void 0,t)});return(a,o)=>(e(),c(f,null,[t(S,{modelValue:W.value,"onUpdate:modelValue":o[1]||=e=>W.value=e,class:`px-6 py-4 flex flex-col gap-2`},{default:u(()=>[o[3]||=s(`div`,{class:`flex gap-2 text-xl`},[s(`div`,{class:`i-carbon:information my-auto`}),r(` Help `)],-1),o[4]||=s(`div`,{class:`prose dark:prose-invert`},[s(`p`,null,`This is the hands-free live notes viewer.`),s(`p`,null,`It's designed to be used in a separate view or device. 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The progress is controlled by and auto synced with the main presenter or slide.`)],-1),s(`div`,D,[s(`button`,{class:`slidev-form-button`,onClick:o[0]||=e=>W.value=!1},` Close `)])]),_:1},8,[`modelValue`]),s(`div`,O,[t(T,{"clicks-context":J.value,current:U.value},null,8,[`clicks-context`,`current`]),t(E),s(`div`,{ref_key:`scroller`,ref:V,class:`px-5 py-3 flex-auto h-full overflow-auto`,style:i({fontSize:`${p(H)}px`})},[t(w,{note:G.value?.meta.slide.note,"note-html":G.value?.meta.slide.noteHTML,placeholder:`No notes for Slide ${U.value}.`,"clicks-context":J.value,"auto-scroll":!0},null,8,[`note`,`note-html`,`placeholder`,`clicks-context`])],4),s(`div`,k,[t(C,{"clicks-context":J.value,readonly:``},null,8,[`clicks-context`])]),s(`div`,A,[s(`div`,j,[t(x,{title:p(z)?`Close fullscreen`:`Enter fullscreen`,onClick:p(B)},{default:u(()=>[p(z)?(e(),c(`div`,M)):(e(),c(`div`,N))]),_:1},8,[`title`,`onClick`]),t(x,{title:`Increase font size`,onClick:K},{default:u(()=>[...o[5]||=[s(`div`,{class:`i-carbon:zoom-in`},null,-1)]]),_:1}),t(x,{title:`Decrease font size`,onClick:q},{default:u(()=>[...o[6]||=[s(`div`,{class:`i-carbon:zoom-out`},null,-1)]]),_:1}),t(x,{title:`Edit notes`,to:`/notes-edit`,target:`_blank`},{default:u(()=>[...o[7]||=[s(`div`,{class:`i-carbon:edit`},null,-1)]]),_:1}),t(x,{title:`Help`,class:`rounded-full`,onClick:o[2]||=e=>W.value=!0},{default:u(()=>[...o[8]||=[s(`div`,{class:`i-carbon:help`},null,-1)]]),_:1}),o[9]||=s(`div`,{class:`flex-auto`},null,-1),s(`div`,P,[s(`span`,F,n(U.value),1),s(`span`,I,` / `+n(p(R)),1)])])])])],64))}});export{L as default}; \ No newline at end of file +import{B as e,C as t,Ct as n,S as r,St as i,T as a,Z as o,_ as s,b as c,dt as l,et as u,g as d,m as f,yt as p}from"../modules/shiki-CJZsCeMT.js";import{U as m,Z as h,t as g,u as _}from"../useNav-DdRarJm8.js";import{h as v}from"../modules/vue-ClnAKIA7.js";import{m as y,o as b}from"../index-Bm7ETj3d.js";import{t as x}from"./IconButton-BwA4p7Ax.js";import{t as S}from"./Modal-cvUjgiyd.js";import{n as C,t as w}from"./NoteDisplay-Dank4r3h.js";import{r as T,t as E}from"./TimerBar-DB-yAKCI.js";var D={class:`flex my-1`},O={class:`h-full flex flex-col`},k={class:`flex-none border-t border-main`,px3:``,py2:``},A={class:`flex-none border-t border-main`},j={class:`flex gap-1 items-center px-6 py-3`},M={key:0,class:`i-carbon:minimize`},N={key:1,class:`i-carbon:maximize`},P={class:`px2 my-auto`},F={class:`text-lg`},I={class:`opacity-50 text-sm`},L=a({__name:`notes`,setup(a){y({title:`Notes - ${h}`});let{slides:L,total:R}=g(),{isFullscreen:z,toggle:B}=_,V=l(),H=v(`slidev-notes-font-size`,18),U=d(()=>b.page),W=l(!1),G=d(()=>L.value.find(e=>e.no===U.value));o(U,()=>{V.value?.scrollTo({left:0,top:0,behavior:`smooth`}),window.scrollTo({left:0,top:0,behavior:`smooth`})});function K(){H.value+=1}function q(){--H.value}let J=d(()=>{let e=b.clicks,t=b.clicksTotal;return m(l(e),void 0,t)});return(a,o)=>(e(),c(f,null,[t(S,{modelValue:W.value,"onUpdate:modelValue":o[1]||=e=>W.value=e,class:`px-6 py-4 flex flex-col gap-2`},{default:u(()=>[o[3]||=s(`div`,{class:`flex gap-2 text-xl`},[s(`div`,{class:`i-carbon:information my-auto`}),r(` Help `)],-1),o[4]||=s(`div`,{class:`prose dark:prose-invert`},[s(`p`,null,`This is the hands-free live notes viewer.`),s(`p`,null,`It's designed to be used in a separate view or device. 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r=this.resolveSelector(t)||this.el,i=this.eventStart.bind(this),a=this.eventMove.bind(this),o=this.eventEnd.bind(this),s=this.eventKeyboard.bind(this),c=this.touchMove.bind(this);r.addEventListener(`pointerdown`,i,{passive:!1}),n.addEventListener(`pointermove`,a,{passive:!1}),n.addEventListener(`pointerup`,o,{passive:!1}),n.addEventListener(`pointercancel`,o,{passive:!1}),n.addEventListener(`keydown`,s,!1),n.addEventListener(`keyup`,s,!1),n.addEventListener(`touchmove`,c,{passive:!1}),this._disposables.push(()=>{r.removeEventListener(`pointerdown`,i),n.removeEventListener(`pointermove`,a),n.removeEventListener(`pointerup`,o),n.removeEventListener(`pointercancel`,o),n.removeEventListener(`keydown`,s,!1),n.removeEventListener(`keyup`,s,!1),n.removeEventListener(`touchmove`,c)}),this._emitter.emit(`mounted`)}unmount(){this._disposables.forEach(e=>e()),this._disposables.length=0,this._elements.length=0,this.el=null,this._emitter.emit(`unmounted`)}on(e,t){return 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e=this._currentNode;e&&(this._appendNode(e),this.commit({undo:()=>this._removeNode(e),redo:()=>this._restoreNode(e)}))}else this.commit(t);this.drawing=!1,this._emitter.emit(`end`),this._emitter.emit(`changed`),this._originalPointerId=null}touchMove(e){Array.from(e.touches).some(e=>e.touchType===`stylus`&&e.identifier===this._originalPointerId)&&e.preventDefault()}acceptsInput(e){return(!this.options.acceptsInputTypes||this.options.acceptsInputTypes.includes(e.pointerType))&&!(this._originalPointerId&&this._originalPointerId!==e.pointerId)}eventKeyboard(e){(this.shiftPressed!==e.shiftKey||this.altPressed!==e.altKey)&&(this.shiftPressed=e.shiftKey,this.altPressed=e.altKey,this.model.point&&this.model.onMove(this.model.point)&&this._emitter.emit(`changed`))}commit(e){this._opStack.length=this._opIndex,this._opStack.push(e),this._opIndex++;let t=this._currentNode;this._currentNode=void 0,this._emitter.emit(`committed`,t)}clear(){this._opStack.length=0,this._opIndex=0,this._elements=[],this.cancel(),this.el.innerHTML=``,this._emitter.emit(`changed`)}cancel(){this._currentNode&&(this.el.removeChild(this._currentNode),this._currentNode=void 0,this._emitter.emit(`canceled`))}dump(){return this.el.innerHTML}load(e){this.clear(),this.el.innerHTML=e}_appendNode(e){let t=this._elements.at(-1);t?t.after(e):this.el.append(e);let n=this._elements.push(e)-1;e.dataset.drauu_index=n.toString()}_removeNode(e){e.remove(),this._elements[+e.dataset.drauu_index]=null}_restoreNode(e){let t=+e.dataset.drauu_index;this._elements[t]=e;for(let n=t-1;n>=0;n--){let t=this._elements[n];if(t){t.after(e);return}}this.el.prepend(e)}};function oe(e){return new ae(e)}var se=l(()=>{let{currentSlideNo:l,isPresenter:m}=c(),h=[`#ff595e`,`#ffca3a`,`#8ac926`,`#1982c4`,`#6a4c93`,`#ffffff`,`#000000`],g=u(`slidev-drawing-enabled`,!1),_=u(`slidev-drawing-pinned`,!1),v=u(`slidev-drawing-brush`,{color:h[0],size:4,mode:`stylus`}),y=n(!1),b=n(!1),x=n(!1),S=n(!1),C=n(`stylus`),w=r(()=>o.drawings.syncAll||m.value),T=!1,E=r({get(){return C.value},set(e){C.value=e,e===`arrow`?(O.mode=`line`,v.value.arrowEnd=!0):(O.mode=e,v.value.arrowEnd=!1)}}),D=i({brush:v,acceptsInputTypes:r(()=>g.value&&(!o.drawings.presenterOnly||m.value)?void 0:[`pen`]),coordinateTransform:!1}),O=a(oe(D));function k(){O.clear(),w.value&&f(l.value,``)}function A(){x.value=O.canRedo(),b.value=O.canUndo(),S.value=!!O.el?.children.length}function j(e){T=!0;let t=p[e||l.value];t==null?O.clear():O.load(t),A(),T=!1}return O.on(`changed`,()=>{if(A(),!T){let 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end of file +import{P as e,Z as t,dt as n,g as r,lt as i,st as a}from"./modules/shiki-CJZsCeMT.js";import{$ as o,g as s,t as c}from"./useNav-DdRarJm8.js";import{E as l,h as u}from"./modules/vue-ClnAKIA7.js";import{c as d,l as f,s as p}from"./index-Bm7ETj3d.js";var m=()=>({emit(e,...t){for(let n=this.events[e]||[],r=0,i=n.length;r{this.events[e]=this.events[e]?.filter(e=>t!==e)}}});function h(e,t){return e-t}function g(e){return e<0?-1:1}function _(e){return[Math.abs(e),g(e)]}function v(){let e=()=>((1+Math.random())*65536|0).toString(16).substring(1);return`${e()+e()}-${e()}-${e()}-${e()}-${e()}${e()}${e()}`}var y=2;function b(e,t){let n=document.createElementNS(`http://www.w3.org/2000/svg`,`defs`),r=document.createElementNS(`http://www.w3.org/2000/svg`,`marker`),i=document.createElementNS(`http://www.w3.org/2000/svg`,`path`);return i.setAttribute(`fill`,t),r.setAttribute(`id`,e),r.setAttribute(`viewBox`,`0 -5 10 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this.el||this.onStart(t),this.points[this.points.length-1]!==t&&(this.points.push(t),this.count+=1),this.count>5&&(this.points=E(this.points,1,!0),this.count=0),this.attr(`d`,e.toSvgData(this.points)),!0}onEnd(){let t=this.el;if(this.el=null,!t)return!1;if(t.setAttribute(`d`,e.toSvgData(E(this.points,1,!0))),!t.getTotalLength()){let{x:e,y:n}=this.points[0],r=this.brush.size/2;t.setAttribute(`d`,`M ${e-r} ${n} a ${r},${r} 0 1,0 ${r*2},0 a ${r},${r} 0 1,0 ${-r*2},0`),t.setAttribute(`fill`,this.brush.color),t.setAttribute(`stroke-width`,`0`)}return!0}static line(e,t){let n=t.x-e.x,r=t.y-e.y;return{length:Math.sqrt(n**2+r**2),angle:Math.atan2(r,n)}}static controlPoint(t,n,r,i){let a=n||t,o=r||t,s=e.line(a,o),c=s.angle+(i?Math.PI:0),l=s.length*.2;return{x:t.x+Math.cos(c)*l,y:t.y+Math.sin(c)*l}}static bezierCommand(t,n,r){let i=e.controlPoint(r[n-1],r[n-2],t),a=e.controlPoint(t,r[n-1],r[n+1],!0);return`C ${i.x.toFixed(y)},${i.y.toFixed(y)} ${a.x.toFixed(y)},${a.y.toFixed(y)} 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r=this.resolveSelector(t)||this.el,i=this.eventStart.bind(this),a=this.eventMove.bind(this),o=this.eventEnd.bind(this),s=this.eventKeyboard.bind(this),c=this.touchMove.bind(this);r.addEventListener(`pointerdown`,i,{passive:!1}),n.addEventListener(`pointermove`,a,{passive:!1}),n.addEventListener(`pointerup`,o,{passive:!1}),n.addEventListener(`pointercancel`,o,{passive:!1}),n.addEventListener(`keydown`,s,!1),n.addEventListener(`keyup`,s,!1),n.addEventListener(`touchmove`,c,{passive:!1}),this._disposables.push(()=>{r.removeEventListener(`pointerdown`,i),n.removeEventListener(`pointermove`,a),n.removeEventListener(`pointerup`,o),n.removeEventListener(`pointercancel`,o),n.removeEventListener(`keydown`,s,!1),n.removeEventListener(`keyup`,s,!1),n.removeEventListener(`touchmove`,c)}),this._emitter.emit(`mounted`)}unmount(){this._disposables.forEach(e=>e()),this._disposables.length=0,this._elements.length=0,this.el=null,this._emitter.emit(`unmounted`)}on(e,t){return 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ae=class{constructor(e={}){this.options=e,this.el=null,this.svgPoint=null,this.eventEl=null,this.shiftPressed=!1,this.altPressed=!1,this.drawing=!1,this._emitter=m(),this._originalPointerId=null,this._models=ie(this),this._opStack=[],this._opIndex=0,this._disposables=[],this._elements=[],this.options.brush||(this.options.brush={color:`black`,size:3,mode:`stylus`}),e.el&&this.mount(e.el,e.eventTarget,e.window)}get model(){return this._models[this.mode]}get mounted(){return!!this.el}get mode(){return this.options.brush.mode||`stylus`}set mode(e){this._models[this.mode].onUnselected(),this.options.brush.mode=e,this.model.onSelected(this.el)}get brush(){return this.options.brush}set brush(e){this.options.brush=e}resolveSelector(e){return typeof e==`string`?document.querySelector(e):e||null}mount(e,t,n=window){if(this.el)throw Error(`[drauu] already mounted, unmount previous target first`);if(this.el=this.resolveSelector(e),!this.el)throw Error(`[drauu] target element not 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r=this.resolveSelector(t)||this.el,i=this.eventStart.bind(this),a=this.eventMove.bind(this),o=this.eventEnd.bind(this),s=this.eventKeyboard.bind(this),c=this.touchMove.bind(this);r.addEventListener(`pointerdown`,i,{passive:!1}),n.addEventListener(`pointermove`,a,{passive:!1}),n.addEventListener(`pointerup`,o,{passive:!1}),n.addEventListener(`pointercancel`,o,{passive:!1}),n.addEventListener(`keydown`,s,!1),n.addEventListener(`keyup`,s,!1),n.addEventListener(`touchmove`,c,{passive:!1}),this._disposables.push(()=>{r.removeEventListener(`pointerdown`,i),n.removeEventListener(`pointermove`,a),n.removeEventListener(`pointerup`,o),n.removeEventListener(`pointercancel`,o),n.removeEventListener(`keydown`,s,!1),n.removeEventListener(`keyup`,s,!1),n.removeEventListener(`touchmove`,c)}),this._emitter.emit(`mounted`)}unmount(){this._disposables.forEach(e=>e()),this._disposables.length=0,this._elements.length=0,this.el=null,this._emitter.emit(`unmounted`)}on(e,t){return 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e=this._currentNode;e&&(this._appendNode(e),this.commit({undo:()=>this._removeNode(e),redo:()=>this._restoreNode(e)}))}else this.commit(t);this.drawing=!1,this._emitter.emit(`end`),this._emitter.emit(`changed`),this._originalPointerId=null}touchMove(e){Array.from(e.touches).some(e=>e.touchType===`stylus`&&e.identifier===this._originalPointerId)&&e.preventDefault()}acceptsInput(e){return(!this.options.acceptsInputTypes||this.options.acceptsInputTypes.includes(e.pointerType))&&!(this._originalPointerId&&this._originalPointerId!==e.pointerId)}eventKeyboard(e){(this.shiftPressed!==e.shiftKey||this.altPressed!==e.altKey)&&(this.shiftPressed=e.shiftKey,this.altPressed=e.altKey,this.model.point&&this.model.onMove(this.model.point)&&this._emitter.emit(`changed`))}commit(e){this._opStack.length=this._opIndex,this._opStack.push(e),this._opIndex++;let t=this._currentNode;this._currentNode=void 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se=l(()=>{let{currentSlideNo:l,isPresenter:m}=c(),h=[`#ff595e`,`#ffca3a`,`#8ac926`,`#1982c4`,`#6a4c93`,`#ffffff`,`#000000`],g=u(`slidev-drawing-enabled`,!1),_=u(`slidev-drawing-pinned`,!1),v=u(`slidev-drawing-brush`,{color:h[0],size:4,mode:`stylus`}),y=n(!1),b=n(!1),x=n(!1),S=n(!1),C=n(`stylus`),w=r(()=>o.drawings.syncAll||m.value),T=!1,E=r({get(){return C.value},set(e){C.value=e,e===`arrow`?(O.mode=`line`,v.value.arrowEnd=!0):(O.mode=e,v.value.arrowEnd=!1)}}),D=i({brush:v,acceptsInputTypes:r(()=>g.value&&(!o.drawings.presenterOnly||m.value)?void 0:[`pen`]),coordinateTransform:!1}),O=a(oe(D));function k(){O.clear(),w.value&&f(l.value,``)}function A(){x.value=O.canRedo(),b.value=O.canUndo(),S.value=!!O.el?.children.length}function j(e){T=!0;let t=p[e||l.value];t==null?O.clear():O.load(t),A(),T=!1}return O.on(`changed`,()=>{if(A(),!T){let 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end of file diff --git a/lecture-01/assets/useDrawings-DXjUMqLI.js b/lecture-01/assets/useDrawings-DXjUMqLI.js deleted file mode 100644 index e97e6de4737adf729c55e160dee08479a9c2f9d6..0000000000000000000000000000000000000000 --- a/lecture-01/assets/useDrawings-DXjUMqLI.js +++ /dev/null @@ -1 +0,0 @@ -import{P as e,Z as t,dt as n,g as r,lt as i,st as a}from"./modules/shiki-CJZsCeMT.js";import{$ as o,g as s,t as c}from"./useNav-CCJY516K.js";import{E as l,h as u}from"./modules/vue-ClnAKIA7.js";import{c as d,l as f,s as p}from"./index-CXT8JKvy.js";var m=()=>({emit(e,...t){for(let n=this.events[e]||[],r=0,i=n.length;r{this.events[e]=this.events[e]?.filter(e=>t!==e)}}});function h(e,t){return e-t}function g(e){return e<0?-1:1}function _(e){return[Math.abs(e),g(e)]}function v(){let e=()=>((1+Math.random())*65536|0).toString(16).substring(1);return`${e()+e()}-${e()}-${e()}-${e()}-${e()}${e()}${e()}`}var y=2;function b(e,t){let 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o=a.matrixTransform(t.getScreenCTM()?.inverse());return{x:o.x*r,y:o.y*r,pressure:e.pressure}}}createElement(e,t){let n=document.createElementNS(`http://www.w3.org/2000/svg`,e),r=t?{...this.brush,...t}:this.brush;return n.setAttribute(`fill`,r.fill??`transparent`),n.setAttribute(`stroke`,r.color),n.setAttribute(`stroke-width`,r.size.toString()),n.setAttribute(`stroke-linecap`,`round`),r.dasharray&&n.setAttribute(`stroke-dasharray`,r.dasharray),n}attr(e,t){this.el.setAttribute(e,typeof t==`string`?t:t.toFixed(y))}_setEvent(e){this.event=e,this.point=this.getMousePosition(e)}_eventDown(e){return this._setEvent(e),this.start=this.point,this.onStart(this.point)}_eventMove(e){return this._setEvent(e),this.onMove(this.point)}_eventUp(e){return this._setEvent(e),this.onEnd(this.point)}},O=class e extends D{constructor(...e){super(...e),this.points=[],this.count=0}onStart(e){if(this.el=this.createElement(`path`,{fill:`transparent`}),this.points=[e],this.brush.arrowEnd){this.arrowId=v();let 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PGR207 Deep Learning 2026 — Lecture 01

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PGR207 Deep Learning 2026 — Lecture 01

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-52.7 113.7 -120 135 -202 -c4 -14.7 6 -23 6 -25c0 -7.3 -7 -11 -21 -11c-8 0 -13.2 0.8 -15.5 2.5 -c-2.3 1.7 -4.2 5.8 -5.5 12.5c-1.3 4.7 -2.7 10.3 -4 17c-12 48.7 -34.8 92 -68.5 130 -s-74.2 66.3 -121.5 85c-10 4 -16 7.7 -18 11c0 8.7 6 14.3 18 17c47.3 18.7 87.8 47 -121.5 85s56.5 81.3 68.5 130c0.7 2 1.3 5 2 9s1.2 6.7 1.5 8c0.3 1.3 1 3.3 2 6 -s2.2 4.5 3.5 5.5c1.3 1 3.3 1.8 6 2.5s6 1 10 1c14 0 21 -3.7 21 -11 -c0 -2 -2 -10.3 -6 -25c-20 -79.3 -65 -146.7 -135 -202l-3 -3h399890z -M100 620v40h399900v-40z M0 241v40h399900v-40zM0 241v40h399900v-40z`,rightarrowabovebar:`M0 241v40h399891c-47.3 35.3-84 78-110 128-16.7 32 --27.7 63.7-33 95 0 1.3-.2 2.7-.5 4-.3 1.3-.5 2.3-.5 3 0 7.3 6.7 11 20 11 8 0 -13.2-.8 15.5-2.5 2.3-1.7 4.2-5.5 5.5-11.5 2-13.3 5.7-27 11-41 14.7-44.7 39 --84.5 73-119.5s73.7-60.2 119-75.5c6-2 9-5.7 9-11s-3-9-9-11c-45.3-15.3-85-40.5 --119-75.5s-58.3-74.8-73-119.5c-4.7-14-8.3-27.3-11-40-1.3-6.7-3.2-10.8-5.5 --12.5-2.3-1.7-7.5-2.5-15.5-2.5-14 0-21 3.7-21 11 0 2 2 10.3 6 25 20.7 83.3 67 -151.7 139 205zm96 379h399894v40H0zm0 0h399904v40H0z`,baraboveshortleftharpoon:`M507,435c-4,4,-6.3,8.7,-7,14c0,5.3,0.7,9,2,11 -c1.3,2,5.3,5.3,12,10c90.7,54,156,130,196,228c3.3,10.7,6.3,16.3,9,17 -c2,0.7,5,1,9,1c0,0,5,0,5,0c10.7,0,16.7,-2,18,-6c2,-2.7,1,-9.7,-3,-21 -c-32,-87.3,-82.7,-157.7,-152,-211c0,0,-3,-3,-3,-3l399351,0l0,-40 -c-398570,0,-399437,0,-399437,0z M593 435 v40 H399500 v-40z -M0 281 v-40 H399908 v40z M0 281 v-40 H399908 v40z`,rightharpoonaboveshortbar:`M0,241 l0,40c399126,0,399993,0,399993,0 -c4.7,-4.7,7,-9.3,7,-14c0,-9.3,-3.7,-15.3,-11,-18c-92.7,-56.7,-159,-133.7,-199, --231c-3.3,-9.3,-6,-14.7,-8,-16c-2,-1.3,-7,-2,-15,-2c-10.7,0,-16.7,2,-18,6 -c-2,2.7,-1,9.7,3,21c15.3,42,36.7,81.8,64,119.5c27.3,37.7,58,69.2,92,94.5z -M0 241 v40 H399908 v-40z M0 475 v-40 H399500 v40z M0 475 v-40 H399500 v40z`,shortbaraboveleftharpoon:`M7,435c-4,4,-6.3,8.7,-7,14c0,5.3,0.7,9,2,11 -c1.3,2,5.3,5.3,12,10c90.7,54,156,130,196,228c3.3,10.7,6.3,16.3,9,17c2,0.7,5,1,9, 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-.slidev-slide-container[data-v-0167a76e]{width:100%;height:100%;position:relative;overflow:hidden}.slidev-slide-content[data-v-0167a76e]{--slidev-slide-container-scale:var(--slidev-slide-scale);transform:translate(-50%, -50%) scale(var(--slidev-slide-scale));--un-bg-opacity:1;background-color:rgb(255 255 255/var(--un-bg-opacity));position:absolute;top:50%;left:50%;overflow:hidden}.dark .slidev-slide-content[data-v-0167a76e]{--un-bg-opacity:1;background-color:rgb(18 18 18/var(--un-bg-opacity))}.disable-view-transition[data-v-a7136532] *{view-transition-name:none!important}.slidev-page[data-v-a7136532]{--slidev-slide-zoom-scale:1;width:calc(100% / var(--slidev-slide-zoom-scale));height:calc(100% / var(--slidev-slide-zoom-scale));transform-origin:0 0;scale:var(--slidev-slide-zoom-scale);--slidev-slide-scale:calc(var(--slidev-slide-container-scale) * var(--slidev-slide-zoom-scale));position:absolute;inset:0} diff --git a/lecture-02/assets/StepButton-BfWgSKte.js 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n=o.includes(t+a);e.forEach(e=>{e.classList.toggle(d,!0),e.classList.toggle(`highlighted`,n),e.classList.toggle(`dishonored`,!n)})})})}),(t,n)=>(e(),a(`div`,{ref_key:`el`,ref:v,class:`slidev-katex-wrapper`},[i(t.$slots,`default`)],512))}});export{v as t}; \ No newline at end of file diff --git a/lecture-02/assets/slidev/Modal-DRjfPn8c.js b/lecture-02/assets/slidev/Modal-DRjfPn8c.js deleted file mode 100644 index d6e483533985b9ee2bc980a981eba1eb16e7e220..0000000000000000000000000000000000000000 --- a/lecture-02/assets/slidev/Modal-DRjfPn8c.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,T as t,U as n,_ as r,b as i,bt as a,h as o,v as s,y as c,yt as l}from"../modules/shiki-CRDHBXw7.js";import{C as u}from"../modules/vue-jGGq3ahv.js";var d={key:0,class:`fixed top-0 bottom-0 left-0 right-0 grid z-modal`},f=t({__name:`Modal`,props:{modelValue:{default:!1},class:{default:``}},emits:[`update:modelValue`],setup(t,{emit:f}){let p=t,m=u(p,`modelValue`,f);function 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x=[`title`],S={key:0,"flex-auto":``},C={"text-primary":``},w={op50:``,"text-sm":``},T={key:0,absolute:``,"inset-y-0":``,"right-0":``,"w-0.5":``,"bg-primary":``,"z-1":``},E=s({__name:`ClicksSlider`,props:{clicksContext:{},readonly:{type:Boolean},active:{type:Boolean,default:!0},resettable:{type:Boolean},compact:{type:Boolean},attached:{type:Boolean}},emits:[`activate`,`reset`],setup(r,{emit:i}){let a=r,s=i,c=p(()=>a.clicksContext.total),E=p(()=>v(0,a.clicksContext.clicksStart,c.value)),D=p(()=>c.value-E.value+1),O=p({get(){return a.resettable&&!a.active||a.clicksContext.current>c.value?-1:a.clicksContext.current},set(e){if(a.resettable&&e<0){s(`reset`),a.clicksContext.current=b;return}s(`activate`),a.clicksContext.current=e}}),k=p(()=>a.resettable&&O.value<0),A=p(()=>y(E.value,c.value+1)),j=f(),M;function N(e){let t=j.value.getBoundingClientRect();return(e.clientX-t.left)/Math.max(1,t.width)}function P(e,t){if(a.readonly||!j.value||!t&&!(e.buttons&1))return;let 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m}from"../modules/shiki-CRDHBXw7.js";import{C as h,k as g,o as _}from"../modules/vue-jGGq3ahv.js";import{t as v}from"../index-BRVUm3_6.js";import{t as y}from"./NoteDisplay-C3x6Bkvc.js";var b=[`placeholder`],x=i({__name:`NoteEditable`,props:{no:{type:Number,required:!0},class:{default:``},editing:{default:!1},style:{default:()=>({})},placeholder:{default:`No notes for this slide`},clicksContext:{type:Object},highlight:{default:!0},autoHeight:{default:!1}},emits:[`update:editing`,`markerDblclick`,`markerClick`],setup(i,{emit:x}){let S=i,C=x,w=h(S,`editing`,C,{passive:!0}),{info:T,update:E}=v(u(S,`no`)),D=l(``),O,{ignoreUpdates:k}=g(D,e=>{if(!w.value)return;let t=S.no;clearTimeout(O),O=setTimeout(()=>{E({note:e},t)},500)});a(()=>T.value?.note,(e=``)=>{w.value||(clearTimeout(O),k(()=>{D.value=e}))},{immediate:!0,flush:`sync`});let A=l(),j=l();n(()=>{w.value&&A.value?.focus()}),_(A,()=>{w.value=!1});function 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a/lecture-02/assets/slidev/VClicks-buBFUjdS.js b/lecture-02/assets/slidev/VClicks-buBFUjdS.js deleted file mode 100644 index 589bf2437aad593c4aa813609b369ea7d065bff4..0000000000000000000000000000000000000000 --- a/lecture-02/assets/slidev/VClicks-buBFUjdS.js +++ /dev/null @@ -1 +0,0 @@ -import{B as e,C as t,G as n,O as r,R as i,T as a,j as o,m as s,p as c,tt as l,v as u,z as d}from"../modules/shiki-CRDHBXw7.js";import{K as f,it as p}from"../useNav-0H1CZeft.js";import{n as m}from"./context-Dpnqq1ol.js";import{u as h}from"../index-BRVUm3_6.js";var g=a({__name:`VClickGap`,props:{size:{type:[String,Number],default:1}},setup(t){let n=t,{$clicksContext:r}=m(),a=h(),o=+n.size;return Number.isNaN(o)&&(console.warn(`[slidev] Invalid size for VClickGap: ${n.size}`),o=1),i(()=>{let 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PGR207 Deep Learning 2026 — Lecture 02

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a/lecture-05/assets/slidev/VClicks-DLDbGwsQ.js b/lecture-05/assets/slidev/VClicks-DLDbGwsQ.js deleted file mode 100644 index ffa20c144883219f77df9fd60eec41d85c1c6f2c..0000000000000000000000000000000000000000 --- a/lecture-05/assets/slidev/VClicks-DLDbGwsQ.js +++ /dev/null @@ -1 +0,0 @@ -import{A as e,D as t,L as n,R as r,S as i,W as a,et as o,m as s,p as c,v as l,w as u,z as d}from"../modules/shiki-CkJq9tSf.js";import{K as f,it as p}from"../useNav-D6eKrDQR.js";import{n as m}from"./context-DnYiQGy6.js";import{u as h}from"../index-CK0S5VQV.js";var g=u({__name:`VClickGap`,props:{size:{type:[String,Number],default:1}},setup(e){let t=e,{$clicksContext:i}=m(),a=h(),o=+t.size;return Number.isNaN(o)&&(console.warn(`[slidev] Invalid size for VClickGap: ${t.size}`),o=1),n(()=>{let 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/dev/null @@ -1 +0,0 @@ -import"../useNav-D6eKrDQR.js";import"./context-DnYiQGy6.js";import"../syncState-BCmj5bTl.js";import"../index-CK0S5VQV.js";import"../useDrawings-D53otB4S.js"; \ No newline at end of file diff --git a/lecture-05/assets/slidev/notes-EANQ1CSk.js b/lecture-05/assets/slidev/notes-EANQ1CSk.js deleted file mode 100644 index 5968b7e1338a63073f466209380c3b6858928653..0000000000000000000000000000000000000000 --- a/lecture-05/assets/slidev/notes-EANQ1CSk.js +++ /dev/null @@ -1 +0,0 @@ -import{$ as e,S as t,St as n,X as r,_ as i,b as a,g as o,m as s,ut as c,vt as l,w as u,x as d,xt as f,z as p}from"../modules/shiki-CkJq9tSf.js";import{$ as m,W as h,t as g,u as _}from"../useNav-D6eKrDQR.js";import{g as v}from"../modules/vue-Dz2ojcKF.js";import{h as y,o as b}from"../index-CK0S5VQV.js";import{t as x}from"./IconButton-CfFb_8Iy.js";import{t as S}from"./Modal-Bum_LA_c.js";import{n as C,t as w}from"./NoteDisplay-bD5KFX7q.js";import{r as T,t as E}from"./TimerBar-D-Mjxk58.js";var 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One session. The arc: the data changed, so the layer has to change; the change -is a single feedback connection; that connection is what lets the network read a -sequence of any length, and also what makes it hard to train.

-

The picture is the running example, review B: "the movie was not great". The -0.22 on the right is P(positive) from the small two-unit network we build by hand -in section 01 — negative, as it should be. Do not explain it now; say "by the -end of section 01 you can compute that number yourself".

-

What the room already knows: the MLP and the neuron, gradient descent, PyTorch -training loops, CNNs, cross-entropy, Adam, and generative models. Nothing here -depends on anything the room has not seen. Attention appears in section 06, in -its original recurrent form only.

-

Do not rush section 02. Vanishing gradients are the reason LSTM and GRU exist, -and a student who has only been told "gradients vanish" cannot see why the fixes -are shaped the way they are.

-

Sections 04 and 05 (embeddings, PyTorch) are the compressible ones if the session -runs late — the lab covers them. Today ends on two walls a recurrent network hits.

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The glyph grid is the map of today, and the same seven glyphs come back on the -section dividers and on the recap. Point at three of them: the loop (section 01, -the one new wire), the fading arrows (section 02, why it is hard to train), and the -valve (section 03, the fix). Those three sections are the lecture — the click -says so. Sections 04 and 05 can shrink to one slide each if time is short — the -lab carries them.

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Section 00, about fifteen minutes. The glyph: word tiles in a row, read left to -right. The middle tile is marked — one word that changes the others.

-

What this section has to land, in order: text is written one token after another; -a fixed window does not fit text; averaging the words loses the order; and two -facts about sequences that become the recurrent layer.

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Start with a model that reads text through a fixed window, the way a 1-D -convolution does. Top row: review B, one word after another, and a window of three -words. To guess the next word it sees "movie was not" and nothing else.

-

Click. Two things break the window for text.

-
    -
  1. Length. A review is 5 words or 500. A window wide enough for every review is -mostly padding for most reviews.
    2. -
  2. Distance. "not" at the start can change a word much later. Here it is 15 words -back, and the window of three cannot see it. Make the window 16 wide and the -next review puts "not" 30 words back.
    3. -
-

Then the callout, which is the question the whole deck answers: carry a running -summary forward — one small vector, updated after every word — instead of a -window. Section 01 builds that summary; the rest of the deck is about training it.

-

At the end of the deck we come back here: generating text one word at a time is -the same idea, predict the next word from the ones before it, with the running -summary in place of the window.

-`,images:[],raw:``,frontmatter:y,filepath:``,start:127,sourceIndex:3,id:3,no:4},__clicksContext:null,__preloaded:!1}),b=u({layout:`default`,title:`Two reviews, one word apart`}),at=d({get layout(){return b.layout},get transition(){return b.transition},get class(){return b.class},get clicks(){return b.clicks},get name(){return b.name},get preload(){return b.preload},slide:{content:``,revision:`t6ozlo`,frontmatterRaw:`layout: default -title: Two reviews, one word apart -`,note:``,title:`Two reviews, one word apart`,index:4,noteHTML:`

The point: this is the running example. Two reviews, the same words except -one, with opposite labels. Write both on the board and leave them there for the -whole session.

-

On screen: review A, "the movie was great" — positive. Review B, "the movie -was not great" — negative. The word strips show them tile by tile, with "not" -highlighted in B.

-

The right-hand picture is the toy embedding: each word becomes two numbers. -Dimension 1 means "a positive word", dimension 2 means "a negation word". The, -movie and was carry no sentiment, so they sit at [0, 0]; great is [1, 0]; not -is [0, 1]. Real embeddings have 64 or more dimensions and are learned — section -04 shows where they come from. Here we set them by hand so the arithmetic fits -on the board.

-

Click: "Same words except one. Opposite labels." Point at the "not" tile in -strip B — it is the only difference.

-

Click 2: before this one appears, get the room to say what "not" does. It has -no sentiment of its own; it changes the meaning of what comes after it. That is -exactly what a model of single words cannot see.

-

Click 3: so the model has to carry something from one word to the next. That -sentence is the motivation for everything that follows.

-

Click 4: the callout — these two reviews run through the whole lecture. Every -section comes back to them, with the same picture.

-

An older pair makes the same point and is worth saying aloud: "dog bites man" -(page 14) and "man bites dog" (front page) — identical words, different news.

-`,images:[],raw:``,frontmatter:b,filepath:``,start:191,sourceIndex:4,id:4,no:5},__clicksContext:null,__preloaded:!1}),x=u({layout:`default`,title:`A bag of words: keep the words, drop the order`}),ot=d({get layout(){return x.layout},get transition(){return x.transition},get class(){return x.class},get clicks(){return x.clicks},get name(){return x.name},get preload(){return x.preload},slide:{content:``,revision:`uiy0gq`,frontmatterRaw:`layout: default -title: "A bag of words: keep the words, drop the order" -`,note:``,title:`A bag of words: keep the words, drop the order`,index:5,noteHTML:`

The point: before testing it, name the simplest way to read text with the -networks the room already has. A bag of words throws away the order of the -words and keeps only which words appear. The next slide tests whether that is -enough for our two reviews.

-

On screen: the top row is review B, word by word. The arrow pours the five words -into a bag; inside the bag they have no order any more. "not" is drawn in red in -the bag because it is the word whose position matters.

-

Click: the second row is the same five words shuffled — "great not was movie -the". It goes into exactly the same bag. That is the definition: two reviews with -the same words, in any order, are the same bag.

-

Click 2: the bag becomes one vector. With the toy embedding, the, movie and was -are [0, 0], not is [0, 1] and great is [1, 0]. Add the five vectors and divide by -five: ([0, 0] + [0, 0] + [0, 0] + [0, 1] + [1, 0]) / 5 = [1, 1] / 5 = [0.2, 0.2]. -That one vector is what a classifier sees. A 4-word review and a 400-word review -both become two numbers, so a plain dense layer can read either.

-

Click 3: the callout. A bag of words is the simplest text model, and it is a -strong baseline in practice. The question for the next slide: it keeps the -word "not", but does it keep what "not" does to the word after it?

-

Variants worth naming if asked: instead of averaging embeddings, the classic -version counts each word of the vocabulary (a vector of 20 000 counts). The -order is lost either way.

-`,images:[],raw:``,frontmatter:x,filepath:``,start:279,sourceIndex:5,id:5,no:6},__clicksContext:null,__preloaded:!1}),S=u({layout:`default`,title:`Averaging loses the order`}),st=d({get layout(){return S.layout},get transition(){return S.transition},get class(){return S.class},get clicks(){return S.clicks},get name(){return S.name},get preload(){return S.preload},slide:{content:``,revision:`8t4gf5`,frontmatterRaw:`layout: default -title: Averaging loses the order -`,note:``,title:`Averaging loses the order`,index:6,noteHTML:`

The arithmetic, for the board. The, movie, was are [0, 0]; not is [0, 1]; great -is [1, 0]. -A: (0 + 0 + 0 + [1, 0]) / 4 = [0.25, 0]. -B: ([0, 1] + [1, 0]) / 5 = [0.2, 0.2].

-

Be honest about click 1: on this pair, averaging works. The two points are -different, and a line at "dimension 2 = 0.1" separates them. But look at what that -line has learned: "a review with a not anywhere in it is negative". That is a -rule about which words occur, not about what not does to the word after it.

-

Click 2 breaks the rule. Add one word, bad = [−1, 0] (a negative word). Then -C = "not bad, the movie was great" is positive, and D = "not great, the movie -was bad" is negative. Same six words, so the same average: -([0, 1] + [−1, 0] + [1, 0]) / 6 = [0, 1/6] = [0, 0.17]. -One point, two opposite labels. No classifier that only sees the average — linear -or not — can label both correctly. And the "any not" rule from click 1 gets C -wrong.

-

A second weakness, for the notes: the average dilutes. In a 500-word review the -not contributes 1/500 of the average, so the signal shrinks with length.

-

What survives: averaging keeps which words occur. It loses the order, and so it -loses which word modifies which.

-`,images:[],raw:``,frontmatter:S,filepath:``,start:365,sourceIndex:6,id:6,no:7},__clicksContext:null,__preloaded:!1}),C=u({layout:`default`,title:`Try it with the network we already have`}),ct=d({get layout(){return C.layout},get transition(){return C.transition},get class(){return C.class},get clicks(){return C.clicks},get name(){return C.name},get preload(){return C.preload},slide:{content:``,revision:`-2bnuct`,frontmatterRaw:`layout: default -title: Try it with the network we already have -`,note:``,title:`Try it with the network we already have`,index:7,noteHTML:`

The point: try the tool the room already has — flatten a review into one long -vector and feed it to a dense layer, exactly as with MNIST. It works on A and -B, but it is huge and it learns every word again at every position.

-

On screen: a window of 200 slots, one word per slot. Review A fills slots 1 to -4, review B slots 1 to 5; the dashed boxes are padding. The highlighted box is -great: slot 4 in A, slot 5 in B.

-

Do the arithmetic in the text aloud. A one-hot vector is 20 000 numbers, all -zero except a single 1 at the word's index. 200 slots x 20 000 = 4 000 000 -inputs. One dense layer of 256 units: 4 000 000 x 256 + 256 biases = -1 024 000 256 weights. A billion, before the second layer and before anything -has been learned.

-

Click: two things appear together — the red marks under slots 4 and 5 in the -picture ("different weights"), and the "Position-locked" line. great sits in -slot 4 in A and slot 5 in B, so different columns of weights read it. The layer -must learn great, and not-before-great, separately at every one of the 200 -positions. This is the complaint that led to CNNs, with "pixel" replaced by -"position", and the answer is the same: share the weights.

-

Click 2: the callout. Be fair to it: unlike the average on the last slide, the -window keeps the order, so it can tell A from B. But it learns each word again -at every position.

-

The slide shows the worst problem (position-locking); say the other two aloud.

-
    -
  1. Fixed length. Every review must be exactly 200 words. Longer ones are cut — -and the cut can remove the word that carries the label. Short ones are -padded, and review A is 196 slots of padding out of 200.
    2. -
  2. Size. Even with a 64-number embedding instead of one-hot (200 x 64 = 12 800 -inputs), the dense layer is 12 800 x 256 + 256 = 3.28 M weights, plus -1.28 M for the embedding. The LSTM classifier we build in section 05 -(embedding 20 000 x 64, LSTM 64 to 128, a linear head to 2 classes) has -1 379 586 ≈ 1.38 M parameters in total — and that number does not change -when the review gets longer.
    3. -
-`,images:[],raw:``,frontmatter:C,filepath:``,start:455,sourceIndex:7,id:7,no:8},__clicksContext:null,__preloaded:!1}),w=u({layout:`default`,title:`Sequence, or time series?`}),lt=d({get layout(){return w.layout},get transition(){return w.transition},get class(){return w.class},get clicks(){return w.clicks},get name(){return w.name},get preload(){return w.preload},slide:{content:``,revision:`cwwkeg`,frontmatterRaw:`layout: default -title: Sequence, or time series? -`,note:``,title:`Sequence, or time series?`,index:8,noteHTML:`

The point: a vocabulary check. A sequence is anything where order matters; a -time series is a sequence on a time axis, where the gaps are data too. The -models today see only positions.

-

On screen: the left picture has positions and nothing else — DNA letters in a -row. The right one has a time axis, and the uneven gap between two samples -(marked in red) is itself information.

-

Click: "the words of a sentence" — our reviews are this kind.

-

Click 2: "a DNA (genetic code) sequence" — order matters, no clock.

-

Click 3: "stock prices, one per minute" — a time series with regular sampling.

-

Click 4: "speech: 16 000 samples a second" — also regular, but very long.

-

Click 5: "an ECG (electrocardiogram) trace" — the heart's electrical signal, -for anyone with clinical interests.

-

Click 6: the callout. Every time series is a sequence, but today's models see -only positions.

-

That distinction matters for one practical reason: with a time series you often -have to decide what to do about irregular sampling and missing steps. A plain -RNN silently treats "one step" as "one row of the tensor", whatever the real -gap was. A patient's vitals, sampled whenever a nurse comes by, is the classic -case. Mention it; do not solve it.

-

More examples if the room wants them: the moves of a chess game (a sequence, no -clock that matters).

-

Short slide: a minute or two.

-`,images:[],raw:``,frontmatter:w,filepath:``,start:541,sourceIndex:8,id:8,no:9},__clicksContext:null,__preloaded:!1}),T=u({layout:`default`,title:`Two facts about sequences`}),ut=d({get layout(){return T.layout},get transition(){return T.transition},get class(){return T.class},get clicks(){return T.clicks},get name(){return T.name},get preload(){return T.preload},slide:{content:``,revision:`56or83`,frontmatterRaw:`layout: default -title: Two facts about sequences -`,note:``,title:`Two facts about sequences`,index:9,noteHTML:`

CNNs did this for images: things are local, and things repeat. Repetition -became parameter sharing — one filter slid over every position. Locality does -not carry over: a sentence is not a neighbourhood, it is an order. Say that in one -breath; it is the only CNN recap the section needs.

-

Click 1, fact one: "not great" is negative at word 4 and at word 140. So one set -of weights W should read every position — the same W in every box. This fixes -the position-locked window from two slides back.

-

Click 2, fact two: what came earlier changes what a later word means. So -something has to be carried from each position to the one after it: the state h, -one small vector passed along the row. It is the running summary from -"One step at a time, through a window".

-

The picture is the recurrent network unrolled over review B, before we have -named it. Section 01 opens on exactly this drawing, folded into one cell with a -loop. A good image for the room: the state is a note passed along a row of -people. Each person reads one word, updates the note, and passes it on.

-

Write the two facts on the board; the rest of the lecture refers back to them by -number.

-`,images:[],raw:``,frontmatter:T,filepath:``,start:635,sourceIndex:9,id:9,no:10},__clicksContext:null,__preloaded:!1}),E=u({layout:`interactive`,heading:`An aside — a convolution can read a sequence too`,title:`An aside — a convolution can read a sequence too`,"aside-width":`19rem`}),dt=d({get layout(){return E.layout},get transition(){return E.transition},get class(){return E.class},get clicks(){return E.clicks},get name(){return E.name},get preload(){return E.preload},slide:{content:``,revision:`85y92c`,frontmatterRaw:`layout: interactive -heading: An aside — a convolution can read a sequence too -title: An aside — a convolution can read a sequence too -aside-width: 19rem -`,note:``,title:`An aside — a convolution can read a sequence too`,index:10,noteHTML:`

The point: this slide exists so nobody leaves believing recurrence is the only -way to read a sequence. A 1-D convolution reads one too — in parallel, but only -as far as its filter reaches.

-

On screen: the 1-D convolution widget, with the axis read as time. One filter -slides along the input and every output uses the same weights — weight sharing -across positions, which already fixes the position-locking problem from the -dense-layer slide. Try the padding and stride controls: padding keeps the -output as long as the input, stride 2 halves it.

-

Click: "Parallel". Each output of the convolution depends only on the few input -words under the filter, and the input is all known in advance. No output needs -another output, so a GPU computes all of them at the same time.

-

Click 2: the reach is limited. The filter's reach is its receptive field: -stacking layers widens it, but it is always a fixed number of positions. If the -"not" is 60 words back, outside the reach, the output never sees it.

-

Click 3: a recurrent layer makes the opposite trade, and the small picture shows it. The -number in each box is the round in which that output can be computed. The -convolution's five outputs are all computed in round 1. The recurrent layer's step 5 needs -the state from step 4, which needs step 3, and so on, so the five steps take five -rounds, one after another. A 500-word review takes 500 rounds, however big the -GPU is. What the RNN gets in return: the state is passed along the whole chain, -so a word from any distance back can still affect the output. Plant the flag for -the last section: this waiting is the second wall at the end of today.

-

If someone offers n-grams: yes, a bigram catches "not good", and that is -exactly what a 1-D convolution with a width-2 filter learns. It will not catch -"not, by any stretch of the imagination, good". Unlimited distance is what -recurrence buys.

-

WaveNet and ByteNet are convolutional; so is most on-device audio.

-`,images:[],raw:``,frontmatter:E,filepath:``,start:697,sourceIndex:10,id:10,no:11},__clicksContext:null,__preloaded:!1}),D=u({layout:`interactive`,heading:`Which shapes does a sequence problem come in?`,title:`Which shapes does a sequence problem come in?`,"aside-width":`16rem`}),ft=d({get layout(){return D.layout},get transition(){return D.transition},get class(){return D.class},get clicks(){return D.clicks},get name(){return D.name},get preload(){return D.preload},slide:{content:``,revision:`-83nn6`,frontmatterRaw:`layout: interactive -heading: Which shapes does a sequence problem come in? -title: Which shapes does a sequence problem come in? -aside-width: 16rem -`,note:``,title:`Which shapes does a sequence problem come in?`,index:11,noteHTML:`

Start on the first tab, "no recurrence". Its caption uses the word IID: -independent and identically distributed. Independent means one example tells you -nothing about the next one; identically distributed means they all come from the -same data. Five Iris flowers are IID, so their order does not matter and -shuffling them changes nothing. The five words of a review are not: shuffle them -and the meaning changes. Everything else in this lecture is about data that is -not IID along the time axis.

-

Click through all five and read the examples. Then ask which one the review -classifier is: many to one — five words in, one label out. Autocomplete is many to -many, in step, shifted by one: predict the next word from the ones before it.

-

The encoder-decoder tab is what the last section of the lecture is about -(translating review B into Norwegian). Flag it and move on.

-`,images:[],raw:``,frontmatter:D,filepath:``,start:768,sourceIndex:11,id:11,no:12},__clicksContext:null,__preloaded:!1}),O=u({layout:`default`,title:`Poll: which model just broke?`}),pt=d({get layout(){return O.layout},get transition(){return O.transition},get class(){return O.class},get clicks(){return O.clicks},get name(){return O.name},get preload(){return O.preload},slide:{content:``,revision:`-7ild8l`,frontmatterRaw:`layout: default -title: "Poll: which model just broke?" -`,note:``,title:`Poll: which model just broke?`,index:12,noteHTML:`

Hands up for each before revealing. The picture: the dashed line is where the -training reviews end; the test review runs past it.

-

Option 3 catches people who have not yet realised there is one cell rather than T -cells — which is exactly what the next section shows. The unrolled picture on -"Two facts about sequences" had five boxes, but they were the same W five times.

-

Option 2 is the one to discuss: the 1D CNN's filters do not break, but if its last -layer flattens the feature map into a dense head, that head breaks for the same -reason as the MLP. Global pooling over time is the usual fix.

-`,images:[],raw:``,frontmatter:O,filepath:``,start:812,sourceIndex:12,id:12,no:13},__clicksContext:null,__preloaded:!1}),k=u({layout:`section`,index:`01`}),mt=d({get layout(){return k.layout},get transition(){return k.transition},get class(){return k.class},get clicks(){return k.clicks},get name(){return k.name},get preload(){return k.preload},slide:{content:``,revision:`-mzuc3r`,frontmatterRaw:`layout: section -index: "01" -`,note:``,title:`The recurrent layer`,level:1,index:13,noteHTML:`

Section 01 is the lecture, part 1. Never cut it. About 25 minutes.

-

The glyph is a cell whose output curls back into its own input: the state. It -comes back on the overview and the recap, so name it now: "this loop is the whole -idea of the lecture".

-

The metaphor for the whole section: a row of people pass a note along. Each one -reads one word of the review, updates the note, and passes it on. The last person -reads the note and says positive or negative. The note is the hidden state.

-`,images:[],raw:``,frontmatter:k,filepath:``,start:870,sourceIndex:13,id:13,no:14},__clicksContext:null,__preloaded:!1}),A=u({layout:`interactive`,heading:`One new wire`,title:`One new wire`,"aside-width":`17rem`}),ht=d({get layout(){return A.layout},get transition(){return A.transition},get class(){return A.class},get clicks(){return A.clicks},get name(){return A.name},get preload(){return A.preload},slide:{content:``,revision:`cqu7he`,frontmatterRaw:`layout: interactive -heading: One new wire -title: One new wire -aside-width: 17rem -`,note:``,title:`One new wire`,index:14,noteHTML:`

The point: a recurrent layer is an ordinary layer with one new wire — its -output feeds back in as an input at the next step.

-

On screen: the widget starts folded. Press nothing yet; let them look at the -loop. The box is one hidden layer, the word comes in from below, and the -output of the layer comes back round into itself.

-

The single most common misreading is that the loop is a second layer or a -memory buffer sitting beside the network. It is neither: it is the same units, -reading the values they held one step ago. In the toy network there are two -units; in the code later there are 128.

-

The note metaphor in the aside: each reader gets one word of review B ("the -movie was not great") and the note from the person before. They write a new -note and pass it on.

-

Click: W_xh reads this word — how the reader reads the word.

-

Click 2: W_hh reads the note, which is the layer's own last output — how they -read the old note.

-

Click 3: W_ho turns the note into an answer — the output layer.

-

Click 4: the callout. Compared with the dense layer, the only thing new is one -extra matrix (W_hh) and one step of delay. "One step of delay" is worth -repeating: without the delay the definition would be circular.

-`,images:[],raw:``,frontmatter:A,filepath:``,start:893,sourceIndex:14,id:14,no:15},__clicksContext:null,__preloaded:!1}),j=u({layout:`interactive`,heading:`The same layer, once per step`,title:`The same layer, once per step`,"aside-width":`18rem`}),gt=d({get layout(){return j.layout},get transition(){return j.transition},get class(){return j.class},get clicks(){return j.clicks},get name(){return j.name},get preload(){return j.preload},slide:{content:``,revision:`-xfp1ds`,frontmatterRaw:`layout: interactive -heading: The same layer, once per step -title: The same layer, once per step -aside-width: 18rem -`,note:``,title:`The same layer, once per step`,index:15,noteHTML:`

The point: unrolling draws one copy of the layer per word. It is the same layer -with the same weights, used once per step.

-

On screen: the widget starts unrolled. Press "Fold it back" and "Unroll it" a -couple of times. Folded is the network; unrolled is the computation. Students -who only ever see one of the two pictures get a specific wrong idea from each: -a memory cell, or five layers with five sets of weights.

-

Click: every W in the unrolled picture is the same matrix — the copies share -it.

-

Click 2: the chain grows with the review; the weights do not. A 1000-word -review is a longer chain, not a bigger model.

-

Click 3: h_0 = 0 — the note starts blank. If someone asks whether h_0 could be -learned: yes, and it sometimes is. Zeros is the default and it is nearly always -fine.

-

Now press "Next step" and walk through review B. "the", "movie", "was" embed as -[0, 0], so the note stays [0, 0]. "not" sets the first unit: h = [0.96, 0]. -"great" arrives with the note already marked, and the state becomes -[0.45, -0.42]. In review A the same "great" arrives with a blank note and gives -[0, 0.76].

-

Click 4: the callout. A and B are the two running reviews: A is "the movie was -great" (positive), B is "the movie was not great" (negative). Both end on the -word great, so at that last step they give the same input x = [1, 0] to the same -weights. The only difference is the note that arrives with it: blank [0, 0] in -A, the not-flag [0.96, 0] in B. Same word, same weights, different note, -opposite answer.

-`,images:[],raw:``,frontmatter:j,filepath:``,start:949,sourceIndex:15,id:15,no:16},__clicksContext:null,__preloaded:!1}),M=u({layout:`default`,title:`The forward pass, written down`}),_t=d({get layout(){return M.layout},get transition(){return M.transition},get class(){return M.class},get clicks(){return M.clicks},get name(){return M.name},get preload(){return M.preload},slide:{content:``,revision:`a7j9g6`,frontmatterRaw:`layout: default -title: The forward pass, written down -`,note:``,title:`The forward pass, written down`,index:16,noteHTML:`

The point: the whole recurrent layer is one equation. The new note is a -squashed sum of this word and the old note.

-

Read it aloud in words first: "h_t equals tanh of W_xh times x_t, plus W_hh -times h_{t-1}, plus a bias b_h" — the new state is a squashed sum of what I am -looking at and what I already knew. The output is "o_t equals W_ho times h_t -plus b_o".

-

On screen: the picture is the same sentence. Old note h_{t-1} in from the left -through W_hh, word x_t in from below through W_xh, the tanh box in the middle, -new note h_t out to the right, and the output o_t going up through W_ho.

-

Nothing on this slide is new mathematics. W_xh x + b is a dense layer; the tanh -is the familiar activation; the only new symbol is W_hh h_{t-1}. Say that -explicitly — it lowers the temperature of the slide considerably.

-

Click: x_t is this word's embedding.

-

Click 2: h_{t-1} is the note — all the layer remembers. Nothing else about the -earlier words survives.

-

Click 3: tanh keeps h between -1 and 1. Why tanh rather than ReLU: h is fed -back into itself, so an unbounded activation can compound to infinity over 200 -steps. tanh keeps the state bounded. It is also part of why the gradients -vanish, which is section 02's problem.

-

Click 4: the callout. Delete the second term, W_hh h_{t-1}, and this is a dense -layer.

-

Click 5: o_t is logits, with no activation. For the review we only use the last -one, o_T, and turn it into a probability with a sigmoid, as in binary -cross-entropy. The next slide names every symbol and puts numbers in.

-`,images:[],raw:``,frontmatter:M,filepath:``,start:1009,sourceIndex:16,id:16,no:17},__clicksContext:null,__preloaded:!1}),N=u({layout:`default`,title:`Reading the equation: one recurrent step`}),vt=d({get layout(){return N.layout},get transition(){return N.transition},get class(){return N.class},get clicks(){return N.clicks},get name(){return N.name},get preload(){return N.preload},slide:{content:``,revision:`pnsk0t`,frontmatterRaw:`layout: default -title: "Reading the equation: one recurrent step" -`,note:``,title:`Reading the equation: one recurrent step`,index:17,noteHTML:`

Read the legend top to bottom as one sentence: at step t, take this word x_t and -the note h_{t-1}, mix them with two learned matrices, add a bias, squash with -tanh. That gives the new note h_t. The output o_t is a plain dense layer on top.

-

Say which symbols are learned: W_xh, W_hh, W_ho and the two biases. x_t is data. -h_t is neither — it is computed, fresh, for every review.

-

Where h_4 comes from (one step earlier, at "not", t = 4): x_4 = [0, 1]. -W_xh x_4 = [00 + 21, 10 + 01] = [2, 0]. h_3 = [0, 0], so W_hh h_3 = [0, 0]. -h_4 = tanh([2, 0]) = [0.964, 0]. That is the value in the legend's h_{t-1} row.

-

Click 9: the callout gives the result of step 5, h_5 = [0.448, -0.419]. Do not -compute it here; the next slide does it one step per click.

-

The -1.446 is the heart of it: it is the not-flag, travelling through the -1.5 -wire, and it beats the +1 that "great" brought on its own.

-

Shapes to say out loud: in the code slides n_x = 64 and n_h = 128, so W_xh is -128 x 64 and W_hh is 128 x 128. Here n_x = 2 and n_h = 2 so it fits on paper.

-`,images:[],raw:``,frontmatter:N,filepath:``,start:1109,sourceIndex:17,id:17,no:18},__clicksContext:null,__preloaded:!1}),P=u({layout:`default`,title:`Working it out: review B at great`}),yt=d({get layout(){return P.layout},get transition(){return P.transition},get class(){return P.class},get clicks(){return P.clicks},get name(){return P.name},get preload(){return P.preload},slide:{content:``,revision:`r4py96`,frontmatterRaw:`layout: default -title: "Working it out: review B at great" -`,note:``,title:`Working it out: review B at great`,index:18,noteHTML:`

The point: the arithmetic of one recurrent step, at the moment that matters — -review B reaching "great" with the note left by "not". Four small steps, one per -click, each a matrix times a vector the room can check by hand. All numbers are -checked in Python.

-

On screen: the picture is the recipe, numbered in the same order as the cards. -The old note h_4 comes in from the left, the word "great" from below. Each is -multiplied by its own weight matrix (steps 1 and 2), the two results are added -and squashed by tanh (step 3), and the new note h_5 is read out as a -probability (step 4).

-

Click: step 1, what the word brings. x_5 = [1, 0]. Multiply each row of W_xh by -x_5: row 1 is 01 + 20 = 0, row 2 is 11 + 00 = 1. So the word brings [0, 1]: -on its own, "great" pushes the sentiment unit up by 1.

-

Click 2: step 2, what the note brings. h_4 = [0.964, 0] (computed at "not", on -the previous slide). Row 1 of W_hh: 0.50.964 + 00 = 0.482. Row 2: -1.50.964 + -0.80 = -1.446. So the note brings [0.482, -1.446]. The -1.446 is the not-flag -travelling through the -1.5 wire.

-

Click 3: step 3, add and squash. Add the two vectors and the bias (zero here): -[0 + 0.482, 1 - 1.446] = [0.482, -0.446]. Apply tanh to each number: -tanh(0.482) = 0.448 and tanh(-0.446) = -0.419. So h_5 = [0.448, -0.419]. The -sentiment unit is negative: the note's -1.446 beat the word's +1.

-

Click 4: step 4, read out. W_ho = [0, 3] reads only the sentiment unit: -00.448 + 3(-0.419) = -1.256, about -1.26. The sigmoid turns this logit into a -probability: sigmoid(-1.26) = 1 / (1 + e^1.26) = 0.22. P(positive) = 0.22, so -review B is read as negative.

-

For contrast, say review A aloud: it reaches "great" with a blank note [0, 0], -so step 2 brings [0, 0], h_5 = tanh([0, 1]) = [0, 0.762], and -sigmoid(3 * 0.762) = 0.91: positive. Same word, same weights, different note.

-`,images:[],raw:``,frontmatter:P,filepath:``,start:1190,sourceIndex:18,id:18,no:19},__clicksContext:null,__preloaded:!1}),F=u({layout:`default`,title:`The same equation, one matrix`}),bt=d({get layout(){return F.layout},get transition(){return F.transition},get class(){return F.class},get clicks(){return F.clicks},get name(){return F.name},get preload(){return F.preload},slide:{content:``,revision:`rrc2lz`,frontmatterRaw:`layout: default -title: The same equation, one matrix -`,note:``,title:`The same equation, one matrix`,index:19,noteHTML:`

Input x_t, previous state h_{t-1} and bias b_h are as before; the only new -symbol is W_h, the two weight matrices placed side by side.

-

It is easy to call this "another way of writing the same" and leave it there. -This is why it is worth writing: it turns the gate equations on the LSTM slides into one -matrix multiply, which is also exactly what nn.LSTM does — one weight_ih and one -weight_hh per layer, not eight.

-

The toy example: the 2 x 4 matrix is W_xh and W_hh side by side. Times -[1, 0, 0.964, 0] (great, then the note after "not"): -row 1: 01 + 20 + 0.50.964 + 00 = 0.482 -row 2: 11 + 00 - 1.50.964 + 0.80 = -0.446 -The same sum as the previous slide, before the tanh.

-

128 x 192: 128 rows because there are 128 units, 192 columns because 64 + 128. -The drawing is to scale. Have them check it.

-`,images:[],raw:``,frontmatter:F,filepath:``,start:1296,sourceIndex:19,id:19,no:20},__clicksContext:null,__preloaded:!1}),I=u({layout:`interactive`,heading:`One step, by hand`,title:`One step, by hand`,"aside-width":`17rem`}),xt=d({get layout(){return I.layout},get transition(){return I.transition},get class(){return I.class},get clicks(){return I.clicks},get name(){return I.name},get preload(){return I.preload},slide:{content:``,revision:`mlh7b1`,frontmatterRaw:`layout: interactive -heading: One step, by hand -title: One step, by hand -aside-width: 17rem -`,note:``,title:`One step, by hand`,index:20,noteHTML:`

Click through "the", "movie", "was" quickly: every line is zeros, and that is -fine — those words carry no sentiment in the toy embedding.

-

At "not", stop on line 3: the word brings [2, 0], the note brings [0, 0]. Line 4 -gives [0.96, 0]. The first unit is now a flag.

-

Then hand "great" to the room: ask for the two vectors on line 3 before pressing. -The answer is [0, 1] from the word and [0.48, -1.45] from the note. The note wins, -and the sentiment ends at -0.42. The readout gives P(positive) = 0.22.

-

Do not skip this widget because the numbers look small. Nobody who cannot do this -can debug an RNN.

-`,images:[],raw:``,frontmatter:I,filepath:``,start:1365,sourceIndex:20,id:20,no:21},__clicksContext:null,__preloaded:!1}),L=u({layout:`default`,title:`What the state actually is`}),St=d({get layout(){return L.layout},get transition(){return L.transition},get class(){return L.class},get clicks(){return L.clicks},get name(){return L.name},get preload(){return L.preload},slide:{content:``,revision:`-u7je4i`,frontmatterRaw:`layout: default -title: What the state actually is -`,note:``,title:`What the state actually is`,index:21,noteHTML:`

The strip builds with the clicks. At click 0 three words are read and both bars -are empty. Click 1 reads "not": the flag bar fills to 0.964. Click 2 reads -"great": the sentiment bar goes red, to -0.419, and P(positive) = 0.22.

-

Compare review A, "the movie was great": the flag never rises, "great" gives -h = [0, 0.762] and P(positive) = sigmoid(3 * 0.762) = 0.91. Same word, opposite -answer, because the note was different.

-

Be honest about the toy: these weights were set by hand so each unit has a name. -A trained network spreads the job over many units, and nobody labels them.

-

The callout is the most important idea in the section. Recurrence does not give a -network unlimited memory; it gives it a fixed budget and makes it learn what to -keep. Gates (section 03) are a better way of choosing. The attention slide in the -last section is refusing to throw anything away.

-`,images:[],raw:``,frontmatter:L,filepath:``,start:1408,sourceIndex:21,id:21,no:22},__clicksContext:null,__preloaded:!1}),R=u({layout:`default`,title:`Counting the parameters`}),Ct=d({get layout(){return R.layout},get transition(){return R.transition},get class(){return R.class},get clicks(){return R.clicks},get name(){return R.name},get preload(){return R.preload},slide:{content:``,revision:`-sbyrza`,frontmatterRaw:`layout: default -title: Counting the parameters -`,note:``,title:`Counting the parameters`,index:22,noteHTML:`

The point: count the weights of a recurrent layer, and notice that the length -of the review does not appear in the count.

-

On screen: the formula reads "n_h times n_x for W_xh, plus n_h times n_h for -W_hh, plus n_h for the bias b_h". The drawing is to scale: one pixel per row -and column. W_xh is 128 x 64 = 8 192, W_hh is the big square, 128 x 128 = -16 384, and the bias is a thin 128 x 1 strip.

-

Click: the sum with our sizes, n_x = 64 and n_h = 128: -128 x 64 + 128 x 128 + 128 = 8 192 + 16 384 + 128 = 24 704.

-

Click 2: T does not appear. A 10-word and a 1000-word review share the same -weights. Same argument as for CNNs, where an image's height and width do not -appear in a convolution's parameter count. Weight sharing always buys this.

-

Click 3: W_hh grows with n_h squared — double the units, four times the -weights. Ask the room first what happens if you double the hidden size. That is -why 128 and 256 are common and 4096 is not.

-

Click 4: nn.RNN prints 24 832. This saves a lab question every year. nn.RNN -keeps bias_ih and bias_hh, which only ever appear as a sum — mathematically -redundant, kept for the fast GPU (cuDNN) kernels. A student who counts by hand -and then calls sum(p.numel()) gets a number that is n_h = 128 too big and -assumes they are wrong. They are not: 24 704 + 128 = 24 832.

-`,images:[],raw:``,frontmatter:R,filepath:``,start:1459,sourceIndex:22,id:22,no:23},__clicksContext:null,__preloaded:!1}),z=u({layout:`default`,title:`Your turn — count them`}),wt=d({get layout(){return z.layout},get transition(){return z.transition},get class(){return z.class},get clicks(){return z.clicks},get name(){return z.name},get preload(){return z.preload},slide:{content:``,revision:`eczvk1`,frontmatterRaw:`layout: default -title: Your turn — count them -`,note:``,title:`Your turn — count them`,index:23,noteHTML:`

The point: an exercise. Count the layers of the classifier we build later, and -discover that the recurrent part is only 7% of the weights.

-

Do the first row together, let them race the rest. Reveal each row after the -room has had a go.

-

Click: nn.RNN(64, 128) = 128 x 64 + 128 x 128 + 2 x 128 = 24 832, with the two -PyTorch bias vectors from the last slide.

-

Click 2: nn.LSTM(64, 128) = 99 328. Be precise about "blocks": an LSTM has -three gates (input, forget, output) plus a candidate, so four blocks of the -RNN's size: 4 x 24 832 = 99 328. "Four gates" is a common slip — there are -three gates. The LSTM itself is the next section.

-

Click 3: nn.GRU(64, 128) = 74 496. A GRU has two gates (update, reset) plus a -candidate: 3 x 24 832 = 74 496.

-

Click 4: nn.Embedding(20000, 64) = 20 000 x 64 = 1 280 000 — a vocabulary of -20 000 words, 64 numbers each.

-

Click 5: the bar — the whole classifier drawn to scale. 1 280 000 / 1 379 586 = -92.8% embedding; 99 328 / 1 379 586 = 7.2% LSTM; and Linear(128, 2) = -128 x 2 + 2 = 258 weights, 0.02%, the sliver at the right.

-

Click 6: the callout. 1 280 000 + 99 328 + 258 = 1 379 586. The recurrent part -we spend the lecture on is 7% of them.

-

A CNN shows the same imbalance the other way round — most of its weights in one -dense head. Where a model's parameters live is rarely where its ideas live. The -93% also explains why pre-trained embeddings mattered so much: it is the part -of the model with the most to learn and the least supervision.

-`,images:[],raw:``,frontmatter:z,filepath:``,start:1541,sourceIndex:23,id:23,no:24},__clicksContext:null,__preloaded:!1}),B=u({layout:`interactive`,heading:`Five ways to wire it`,title:`Five ways to wire it`,"aside-width":`21rem`}),Tt=d({get layout(){return B.layout},get transition(){return B.transition},get class(){return B.class},get clicks(){return B.clicks},get name(){return B.name},get preload(){return B.preload},slide:{content:``,revision:`-qu9nj3`,frontmatterRaw:`layout: interactive -heading: Five ways to wire it -title: Five ways to wire it -aside-width: 21rem -`,note:``,title:`Five ways to wire it`,index:24,noteHTML:`

The point: the recurrence can come from different places, and one cell can be -reused by stacking or by reading both directions. nn.RNN uses hidden to hidden.

-

On screen: the wiring widget, with one tab per wiring: hidden → hidden, output -→ hidden, output → output, stacked, bidirectional. All three recurrent arrows -drawn at once on one figure is unreadable, so take them one tab at a time and -say which one nn.RNN uses. Walk the first three tabs, then the last two.

-

Click: hidden → hidden, as in nn.RNN. In the note picture, it passes the whole -note on.

-

Click 2: output → hidden passes only the reader's one-word verdict, so -everything the past contributes is squeezed through the output layer, which is -usually far smaller than h. Its upside, shown in the widget's note: it is cheap -to train in parallel when the true outputs are known (teacher forcing).

-

Click 3: output → output, the third tab. Only the previous output feeds the next -output; the hidden layer carries nothing across time. Rare on its own — the idea -survives in models that generate one word at a time and feed each word back in.

-

Click 4: stacked and bidirectional are constructor arguments (num_layers, -bidirectional=True). Stacked: one recurrent layer's states are the next one's -inputs. Bidirectional: a second row of readers going right to left, and the two -notes are joined at each word. Concatenation, not addition, is how the two -directions combine — so the output width doubles and the head has to know that.

-

Click 5: the callout. Bidirectional needs the whole sequence first, so it -cannot predict the next word. That is why it is fine for review B — the whole -review is there before we classify it — and wrong for generating text.

-

The bidirectional caveat catches people out in their own projects: they add -bidirectional=True to a language model, the loss collapses to nothing, and it -takes an afternoon to realise the model can see the answer.

-`,images:[],raw:``,frontmatter:B,filepath:``,start:1615,sourceIndex:24,id:24,no:25},__clicksContext:null,__preloaded:!1}),V=u({layout:`section`,index:`02`}),Et=d({get layout(){return V.layout},get transition(){return V.transition},get class(){return V.class},get clicks(){return V.clicks},get name(){return V.name},get preload(){return V.preload},slide:{content:``,revision:`-20iu3b`,frontmatterRaw:`layout: section -index: "02" -`,note:``,title:`Training through time`,level:1,index:25,noteHTML:`

Section 02 is the lecture, part 2. Never cut it. About 25 minutes.

-

The glyph: a chain of cells with a gradient arrow running back from the loss, -fainter at every step. Keep the note metaphor going: the training signal is a -message passed back down the row, and every person who copies it makes the copy -fainter.

-

The number to aim at is 0.012, on "What vanishing feels like". Section 03 answers -it with 0.82.

-`,images:[],raw:``,frontmatter:V,filepath:``,start:1677,sourceIndex:25,id:25,no:26},__clicksContext:null,__preloaded:!1}),H=u({layout:`default`,title:`The loss for a whole sequence`}),Dt=d({get layout(){return H.layout},get transition(){return H.transition},get class(){return H.class},get clicks(){return H.clicks},get name(){return H.name},get preload(){return H.preload},slide:{content:``,revision:`-4xw25k`,frontmatterRaw:`layout: default -title: The loss for a whole sequence -`,note:``,title:`The loss for a whole sequence`,index:26,noteHTML:`

The point: nothing new about the loss itself — it is cross-entropy, once or T -times. What is new is that one weight matrix now receives gradient from T -different places.

-

On screen, left: one label per review. Five cells in a chain, and only the last -one feeds a loss. Read the equation as "L equals the loss of the last output -o_T against the label y".

-

On screen, right: one label per word. Every cell feeds its own loss. Read it as -"L equals one over T times the sum, over every step t, of the loss of o_t -against y_t". Per-word labels: a tag on every word, or the next character in a -text model.

-

Click: the left box — one term, at the last word. Review B: the label is -negative and the toy network says P(positive) = 0.22, so the chance it gave the -right answer is 0.78, and the loss is -ln 0.78 = 0.25. (Checked: -sigmoid(3 x -0.419) = 0.222; -ln(0.778) = 0.251.)

-

Click 2: the right box — T terms, averaged, and each is a cross-entropy. -Mention the averaging: sum and mean differ by a factor of T, which changes the -effective learning rate. PyTorch's default is mean, and with padded batches -"mean over what" becomes a real question — the padding slide in the PyTorch -section.

-

Click 3: the callout. Either way, one set of weights receives gradient from -every term. The next slide names every symbol in these two equations.

-`,images:[],raw:``,frontmatter:H,filepath:``,start:1700,sourceIndex:26,id:26,no:27},__clicksContext:null,__preloaded:!1}),U=u({layout:`default`,title:`Reading the equation: the sequence loss`}),Ot=d({get layout(){return U.layout},get transition(){return U.transition},get class(){return U.class},get clicks(){return U.clicks},get name(){return U.name},get preload(){return U.preload},slide:{content:``,revision:`-ax5re7`,frontmatterRaw:`layout: default -title: "Reading the equation: the sequence loss" -`,note:``,title:`Reading the equation: the sequence loss`,index:27,noteHTML:`

The legend has nothing new in it except the index t. The loss l is -cross-entropy: minus the log of the probability the model gave the right class. -o_t are the logits at step t; y_t is the right answer at step t.

-

Two worked examples. Character by character: the model is fairly sure at step 1 -(0.8), unsure at step 2 (0.5), and mostly wrong at step 3 (0.25). The worst step -costs the most: 1.39 of the 2.30 total. Averaging gives 0.77. The sum is ln 10 = -2.30 exactly, because 0.8 x 0.5 x 0.25 = 0.1 — a nice check if someone asks.

-

Review B, one label: o_5 = -1.26, sigmoid gives P(positive) = 0.22. The right -answer is negative, with probability 0.78. Loss 0.25. With one label, T plays no -part: the average over one term is that term.

-

Sum versus mean: PyTorch's cross_entropy averages by default. With padded batches -the question "average over what?" becomes real; that is the padding slide in the -PyTorch section.

-`,images:[],raw:``,frontmatter:U,filepath:``,start:1797,sourceIndex:27,id:27,no:28},__clicksContext:null,__preloaded:!1}),W=u({layout:`default`,title:`Backpropagation through time`}),kt=d({get layout(){return W.layout},get transition(){return W.transition},get class(){return W.class},get clicks(){return W.clicks},get name(){return W.name},get preload(){return W.preload},slide:{content:``,revision:`77rlng`,frontmatterRaw:`layout: default -title: Backpropagation through time -`,note:``,title:`Backpropagation through time`,index:28,noteHTML:`

The point: the unrolled network is just a deep feed-forward network, so -ordinary backpropagation trains it. The one twist is that W_hh is shared by -every step, and that twist is where the trouble starts.

-

Backpropagation through time is often shortened to BPTT. Do not derive this. -Point at three things: the outer sum, the inner sum, and the Jacobian in the -middle.

-

On screen: the equation reads "the gradient of the loss with respect to W_hh is -a sum over every time step t; for each one, a sum over every earlier step k of: -how the loss at t depends on h_t, times how h_t depends on h_k, times how h_k -depends on W_hh directly." The underbrace marks the middle factor as the trip -back through t - k steps.

-

The picture is review B: five copies of the cell, the loss L at the end, and the -gradient running back through each W_hh arrow (four of them, hence "W_hh x 4"). -Every red arrow is one factor in the product.

-

Click: the outer sum. W_hh was used at every step, so its gradient collects a -contribution from every step — the same rule as any shared weight, like a -convolution filter used at every position.

-

Click 2: the inner factor dh_t/dh_k, the cost of travelling back from step t to -step k. Point at the underbrace.

-

Click 3: that factor is a product of t - k terms — that is the trouble. The -Jacobian is the whole story:

-

dh_t/dh_k = prod_{j=k+1..t} diag(tanh'(z_j)) W_hh^T

-

Write that on the board. It is a product of (t - k) copies of the same matrix, -each pre-multiplied by a diagonal of tanh derivatives, every one of which is at -most 1. The legend slide works it for one unit; "What vanishing feels like" puts -it on a long review.

-`,images:[],raw:``,frontmatter:W,filepath:``,start:1871,sourceIndex:28,id:28,no:29},__clicksContext:null,__preloaded:!1}),G=u({layout:`default`,title:`Reading the equation: backpropagation through time`}),At=d({get layout(){return G.layout},get transition(){return G.transition},get class(){return G.class},get clicks(){return G.clicks},get name(){return G.name},get preload(){return G.preload},slide:{content:``,revision:`3ta3xt`,frontmatterRaw:`layout: default -title: "Reading the equation: backpropagation through time" -`,note:``,title:`Reading the equation: backpropagation through time`,index:29,noteHTML:`

This example is deliberately smaller than the review: one hidden unit, no tanh, -so the chain rule fits on one line. The review comes back on the next slide.

-

One hidden unit, no tanh, so h_t = W_hh h_{t-1} + x_t. Inputs 1, 0, 0 give -h_1 = 1, h_2 = 0.5, h_3 = 0.25. The loss is just L = h_3, so only the t = 3 term -of the outer sum is non-zero, and dL_3/dh_3 = 1.

-

The inner sum has one term per use of W_hh, k = 1, 2, 3. Each term is -(factor for travelling back from step 3 to step k) x (direct effect at step k): -k = 1: 0.5^2 x h_0 = 0.25 x 0 = 0 -k = 2: 0.5^1 x h_1 = 0.5 x 1 = 0.5 -k = 3: 0.5^0 x h_2 = 1 x 0.5 = 0.5 -Total 1.0. The check on the slide is the point: h_3 = W_hh^2 as a function of the -weight, its derivative is 2 W_hh = 1.0, and the sum over k is the chain rule -doing exactly that bookkeeping.

-

The red factors are 1, 0.5, 0.25: a power of W_hh. With 40 steps instead of 3 that -is 0.5^40, about 1e-12.

-

With a real tanh RNN, each factor also carries tanh'(z), which is at most 1, and -W_hh is a matrix, so the factor is a matrix product. Do not go further than that.

-`,images:[],raw:``,frontmatter:G,filepath:``,start:1957,sourceIndex:29,id:29,no:30},__clicksContext:null,__preloaded:!1}),K=u({layout:`default`,title:`What vanishing feels like`}),jt=d({get layout(){return K.layout},get transition(){return K.transition},get class(){return K.class},get clicks(){return K.clicks},get name(){return K.name},get preload(){return K.preload},slide:{content:``,revision:`-1ij0p`,frontmatterRaw:`layout: default -title: What vanishing feels like -`,note:``,title:`What vanishing feels like`,index:30,noteHTML:`

The point: on a long review the word that decides the label is 20 steps before -the end, and the training signal that would teach the network to remember it -arrives at about 1% strength. Forward the state could carry it; backward the -learning signal cannot survive the trip.

-

On screen: a 21-word review. "not" is the first word, highlighted; "great" is -the last. The loss sits after "great", so the gradient has to travel 20 steps -back to reach "not".

-

Click: the bar chart under the words — the gradient reaching each word from the -loss. The bars are to scale: 0.8^k for k steps back, so "great" gets 1, five -words back 0.33, ten back 0.11, and "not" 0.012. Use the note metaphor: going -backwards, the loss passes a correction down the row. Every person copies it and -the copy is fainter. Twenty copies later, the person who read "not" gets 1% of -the message.

-

Click 2: the callout names the simplification: each step back multiplies by 0.8, -the sentiment unit's self-weight, and 0.8^20 = 0.012.

-

Click 3: the punchline. The layer could carry the flag forward; it never -receives the training signal that says it should.

-

Be honest — this is a simplification, and say so if asked:

-
    -
  • The real factor per step is a 2 x 2 matrix: diag(tanh'(z)) W_hh. tanh' is at -most 1, so the real factor is smaller, not larger. Computed for this exact -review with the toy weights, the sentiment-to-sentiment entry of dh_21/dh_1 is -about 2e-5 with tanh' included, against 0.8^20 = 0.012 without it.
    • -
  • There are cross-terms: the flag reaches the sentiment through the -1.5 wire. -The flag-to-sentiment entry is about 0.06 without tanh', and 0.002 with it. -So 0.012 is the optimistic number. The next two slides make the general case.
    • -
-

The toy network also forgets forwards: its flag halves at every word (self-weight -0.5), so on this review it says P(positive) = 0.91 — wrong. Training would have to -change W_hh to hold the flag. The point of the slide is that the gradient that -would ask for that change arrives at 1% strength or less.

-

The classic version of this (Olah's): "I grew up in France ... [forty words] ... -so I speak fluent French." The word that decides the answer is forty steps back. -A plain RNN is big enough to represent it; it never receives the signal to learn -it. Fix the backward path and the forward one takes care of itself.

-`,images:[],raw:``,frontmatter:K,filepath:``,start:2047,sourceIndex:30,id:30,no:31},__clicksContext:null,__preloaded:!1}),q=u({layout:`interactive`,heading:`One number, raised to a power`,title:`One number, raised to a power`,"aside-width":`19rem`}),Mt=d({get layout(){return q.layout},get transition(){return q.transition},get class(){return q.class},get clicks(){return q.clicks},get name(){return q.name},get preload(){return q.preload},slide:{content:``,revision:`hm4s8g`,frontmatterRaw:`layout: interactive -heading: One number, raised to a power -title: One number, raised to a power -aside-width: 19rem -`,note:``,title:`One number, raised to a power`,index:31,noteHTML:`

The point: with one unit the backward factor is a single weight W raised to the -power of the distance, so the damage is exponential in distance — and only -W = 1 exactly is safe.

-

On screen: the GradientFlow widget. It opens on the review's number: W = 0.8, -twenty steps, and the readout says 0.8^20 = 0.012. The chart shows the gradient -strength against steps back on a log axis: a straight line whose slope is -log W, so the damage is exponential in the distance, not linear. The aside says -the same in words: k steps back, the gradient is multiplied by W^k.

-

Click: W < 1 means vanishing. Drag W down and watch the line tilt steeper. Ask -how far back a 0.9 weight reaches at 1% strength: about 43 steps. At 0.6, nine. -At 0.8, twenty — exactly the review.

-

Click 2: W > 1 means exploding. Drag above 1 and the line climbs instead. -Exploding happens, but it announces itself with a NaN (not a number); vanishing -is silent.

-

Click 3: W = 1 is stable, but nothing in training keeps the weight there.

-

Click 4: the callout — drag W slowly through 1 so the room sees the line swing -from falling to rising. There is no safe band, only a point.

-

Then say the part that makes it worse: the real factor is W times tanh'(z), and -tanh' is at most 1 and usually well under it. So the effective W is smaller than -the weight, and vanishing is the common case by a wide margin.

-

Tick gradient clipping with W above 1 and point out that the vanishing end of the -chart does not move at all: clipping fixes exploding, never vanishing.

-`,images:[],raw:``,frontmatter:q,filepath:``,start:2125,sourceIndex:31,id:31,no:32},__clicksContext:null,__preloaded:!1}),J=u({layout:`interactive`,heading:`And tanh is not helping`,title:`And tanh is not helping`,"aside-width":`19rem`}),Nt=d({get layout(){return J.layout},get transition(){return J.transition},get class(){return J.class},get clicks(){return J.clicks},get name(){return J.name},get preload(){return J.preload},slide:{content:``,revision:`p5gb2r`,frontmatterRaw:`layout: interactive -heading: And tanh is not helping -title: And tanh is not helping -aside-width: 19rem -`,note:``,title:`And tanh is not helping`,index:32,noteHTML:`

The point: the real factor per step is W times tanh', and tanh' is at most 1 — -so the activation itself shrinks the gradient further, and a confident unit -passes almost nothing back.

-

On screen: the ActivationExplorer widget. Choose Tanh. The solid curve is tanh, -the dashed curve is its derivative. The vanishing-gradient story is usually told -about depth; this widget shows the same fact about length.

-

Click: the derivative peaks at 1, at input 0, and falls away on both sides. -Point at the top of the dashed curve.

-

Click 2: a saturated unit — one whose input is far from 0, so tanh sits near -+1 or -1 — has a derivative near zero. Move along the flat part of the curve.

-

Click 3: every backward step multiplies by one of these derivatives, once per -step, so the shrinking compounds.

-

Click 4: the callout. The real factor is W times tanh', with tanh' at most 1, so -the 0.012 on the long review was the optimistic case.

-

Tie it to the review: after "not" the flag unit sits at 0.964, where tanh' is -1 - 0.964^2 = 0.07. A unit that is sure of itself passes almost no gradient back. -What keeps the state stable is what kills the gradient.

-

The obvious question — why not ReLU, whose derivative is exactly 1 — has a good -answer: people do, and it works with careful initialisation (IRNN), but an -unbounded activation fed back into itself for 200 steps also explodes forwards, not -just backwards. tanh is the safe default and gating is the real fix.

-`,images:[],raw:``,frontmatter:J,filepath:``,start:2185,sourceIndex:32,id:32,no:33},__clicksContext:null,__preloaded:!1}),Y=u({layout:`default`,title:`Three ways out, honestly ranked`}),Pt=d({get layout(){return Y.layout},get transition(){return Y.transition},get class(){return Y.class},get clicks(){return Y.clicks},get name(){return Y.name},get preload(){return Y.preload},slide:{content:``,revision:`-vu8f0s`,frontmatterRaw:`layout: default -title: Three ways out, honestly ranked -`,note:``,title:`Three ways out, honestly ranked`,index:33,noteHTML:`

The point: there are three standard responses to the gradient problem, and they -are not equals. Being explicit about the ranking matters: a list of three fixes -side by side invites a student to pick one.

-

On screen: three columns, each revealed on its own click, each with a small -picture. Nothing is visible but the heading at first.

-

Click: gradient clipping. The picture is a tall red arrow cut off at a dashed -ceiling; the accent part below the line is what survives. Cap the gradient's -length. It fixes exploding only — a gradient that is already tiny is never above -the cap. Clipping is not a hack: it is standard in essentially every recurrent -training script.

-

Click 2: truncated BPTT (backpropagation through time, from the "Backpropagation -through time" slide). The picture is a chain of four cells with a red cut after -the second. Backpropagate k steps, then cut; nothing beyond k steps is learned. -Truncation is a memory and compute decision first and a gradient decision second. -It is the last slide of this section.

-

Click 3: gated cells, with the gate emblem. Make the gradient's path add instead -of multiply. This is the real fix and the next section. Gating is the only one of -the three that touches the 0.012: section 03 turns it into 0.82.

-

Click 4: the callout gives the practical rule. Use clipping and a gated cell -together; truncate only when a sequence is too long for memory.

-`,images:[],raw:``,frontmatter:Y,filepath:``,start:2244,sourceIndex:33,id:33,no:34},__clicksContext:null,__preloaded:!1}),Ft=u({layout:`default`,title:`Gradient clipping, in one line`}),It=d({get layout(){return Ft.layout},get transition(){return Ft.transition},get class(){return Ft.class},get clicks(){return Ft.clicks},get name(){return Ft.name},get preload(){return Ft.preload},slide:{content:``,revision:`waxy1x`,frontmatterRaw:`layout: default -title: Gradient clipping, in one line -`,note:``,title:`Gradient clipping, in one line`,index:34,noteHTML:`

The point: clipping is one line of code, and the only way to get it wrong is to -put it in the wrong place — between backward() and step(), nowhere else.

-

On screen: a training step on the left, and on the right a picture of what -clipping does. The picture: a gradient of length 900 (red) with cap c = 1 -becomes length 1 (accent), pointing exactly where it did. The dashed circle is -every vector of length 1.

-

Click: lines 1-3 — the forward pass, the loss, and zero_grad(). Nothing new.

-

Click 2: line 5, loss.backward(). Only now do the gradients exist.

-

Click 3: line 6, clip_grad_norm_ with max_norm=1.0. It rescales all the -gradients together so their combined length is at most 1.

-

Click 4: line 7, optimiser.step() — it uses the gradients, which are now bounded.

-

Click 5: the whole block again. Read the order top to bottom once more.

-

Click 6: the callout. It goes between backward() and step(): before, there is -nothing to clip; after, it is too late. The placement is the bug students -actually write. Say it twice.

-

Click 7: the rule as a formula. Read it: "if the length of g is bigger than c, -replace g by c times g divided by its length". Symbols: g is the gradient of all -parameters, stacked into one long vector; ||g|| is its length; c is the cap, -max_norm in the code. Dividing by the length makes a vector of length 1, and -multiplying by c makes it length c — same direction, capped length. With the -picture's numbers: 1 x g / 900 has length 1.

-

Norm clipping, not value clipping: clip_grad_value_ exists and clamps each -component independently, which changes the direction of the update. Almost nobody -wants that.

-

max_norm between 0.25 and 5 is the usual range, and 1.0 is a fine default. If -clipping fires on every batch, the learning rate is too high — clipping is a -safety device for rare spikes, not a way to train.

-`,images:[],raw:``,frontmatter:Ft,filepath:``,start:2343,sourceIndex:34,id:34,no:35},__clicksContext:null,__preloaded:!1}),Lt=u({layout:`default`,title:`Truncated backpropagation through time`}),Rt=d({get layout(){return Lt.layout},get transition(){return Lt.transition},get class(){return Lt.class},get clicks(){return Lt.clicks},get name(){return Lt.name},get preload(){return Lt.preload},slide:{content:``,revision:`lbbvzc`,frontmatterRaw:`layout: default -title: Truncated backpropagation through time -`,note:``,title:`Truncated backpropagation through time`,index:35,noteHTML:`

The point: a very long sequence cannot be backpropagated in one go, so it is cut -into chunks. Truncated BPTT truncates learning, not memory: the state still -crosses every chunk boundary, only the gradient stops.

-

On screen: the problem in one line — a 100 000-character book is one sequence, -and 100 000 steps of activations do not fit in memory. Left, the loop over -chunks of 128 steps. Right, the picture: the accent line is the state, flowing -forward through every chunk. The red arrows are the gradient, flowing back only -inside each chunk of 128 steps. The dashed cuts are the detach() calls.

-

Click: line 3 — skip the cut on the very first chunk, when there is no state yet.

-

Click 2: lines 4-5 — detach the state: keep the values, cut the graph. detach() -returns a tensor sharing the same storage with no graph history. The tuple is -there because an LSTM's state is a pair, (h, c) — next section.

-

Click 3: line 6 — run the model on the chunk, starting from the carried-over -state, and get the new state back.

-

Click 4: the whole loop again; the loss, backward and step happen once per chunk.

-

Click 5: the state still flows forward across chunks, so the model's context is -not cut. This is the thing to get right. In the note metaphor: the note keeps -being passed along the whole row, but a correction only travels back as far as -the start of the current chunk.

-

Click 6: only the gradient stops at the boundary — that is what detach() does. -Point at the dashed lines.

-

Click 7: forget the detach and the graph grows across every chunk until the run -runs out of memory. This is also the answer to "why is my loop using more memory -every iteration" for anyone who ever accumulates losses in a list.

-

k = 128 or 256 is typical for character models. It is a memory decision.

-`,images:[],raw:``,frontmatter:Lt,filepath:``,start:2439,sourceIndex:35,id:35,no:36},__clicksContext:null,__preloaded:!1}),zt=u({layout:`section`,index:`03`}),Bt=d({get layout(){return zt.layout},get transition(){return zt.transition},get class(){return zt.class},get clicks(){return zt.clicks},get name(){return zt.name},get preload(){return zt.preload},slide:{content:``,revision:`bv7knv`,frontmatterRaw:`layout: section -index: "03" -`,note:``,title:`Gated cells`,level:1,index:36,noteHTML:`

Section 03 is the third part of the lecture proper, with section 01 and 02. Do -not compress it: it holds the payoff slide, "The highway, against the chain".

-

The glyph is a valve on a line. It comes back on the overview and the recap. -Say the metaphor out loud once more: the state is a note passed along a row of -people; in section 02 it got fainter with every copy. This section puts the -important part of the note in a sealed envelope that only the gates open.

-`,images:[],raw:``,frontmatter:zt,filepath:``,start:2537,sourceIndex:36,id:36,no:37},__clicksContext:null,__preloaded:!1}),Vt=u({layout:`default`,title:`Stop multiplying, start adding`}),Ht=d({get layout(){return Vt.layout},get transition(){return Vt.transition},get class(){return Vt.class},get clicks(){return Vt.clicks},get name(){return Vt.name},get preload(){return Vt.preload},slide:{content:``,revision:`1ebgku`,frontmatterRaw:`layout: default -title: Stop multiplying, start adding -`,note:``,title:`Stop multiplying, start adding`,index:37,noteHTML:`

Derive the architecture rather than presenting it. Students who meet the LSTM as -six equations memorise six equations; students who watch "additive path, then gate -it" can rebuild them.

-

Top row: the plain RNN. h_t = tanh(W_hh h_{t-1} + ...), so going back k words -multiplies the gradient by W_hh (and by tanh') k times. With the sentiment unit's -self-weight 0.8, that is section 02's 0.8^20 = 0.012. The boxes fade: the note -gets fainter with every copy.

-

Click: the bottom row. If instead the state were carried and only added to, -c_t = c_{t-1} + something, then dc_t / dc_{t-1} = 1 and the gradient walks back -twenty words unchanged. The envelope is the same envelope at every step.

-

Click: the callout. A pure sum never forgets anything, so it fills up. The fix is -a valve, learned, on each path: that is the next slide.

-

If the room has met a residual (skip) connection, it is the same additive trick. -Say so in one sentence; do not lean on it.

-`,images:[],raw:``,frontmatter:Vt,filepath:``,start:2558,sourceIndex:37,id:37,no:38},__clicksContext:null,__preloaded:!1}),Ut=u({layout:`interactive`,heading:`The LSTM memory block`,title:`The LSTM memory block`,"aside-width":`21rem`}),Wt=d({get layout(){return Ut.layout},get transition(){return Ut.transition},get class(){return Ut.class},get clicks(){return Ut.clicks},get name(){return Ut.name},get preload(){return Ut.preload},slide:{content:``,revision:`w597ul`,frontmatterRaw:`layout: interactive -heading: The LSTM memory block -title: The LSTM memory block -aside-width: 21rem -`,note:``,title:`The LSTM memory block`,index:38,noteHTML:`

Walk all six, reading each equation as it lights up. Then drive the two extremes -from the aside. They take ten seconds each and they are what the room remembers.

-

Say clearly that the sliders set the gate values. In a real cell each gate is -its own sigma(Wx + Wh + b): a small dense layer with a sigmoid. There -are three gates (forget, input, output) plus the candidate, which is the same -kind of layer with tanh. Four weight sets: that is the "4x" in the parameter -count.

-

The gate is per unit, not per layer. With 128 units there are 128 independent -forget decisions every step, and in a trained model they genuinely differ: some -units hold values for hundreds of steps, most for two or three.

-

Running example, in words only: on the unit that holds the not-flag, a trained -cell can keep the forget gate near 1 from "not" until "great". The flag then -arrives intact. We have not set LSTM weights for the review, so give no numbers -for it.

-`,images:[],raw:``,frontmatter:Ut,filepath:``,start:2631,sourceIndex:38,id:38,no:39},__clicksContext:null,__preloaded:!1}),Gt=u({layout:`default`,title:`The six equations`}),Kt=d({get layout(){return Gt.layout},get transition(){return Gt.transition},get class(){return Gt.class},get clicks(){return Gt.clicks},get name(){return Gt.name},get preload(){return Gt.preload},slide:{content:``,revision:`o52ly`,frontmatterRaw:`layout: default -title: The six equations -`,note:``,title:`The six equations`,index:39,noteHTML:`

The point: the LSTM looks like six equations, but four of them are the same -layer with different weights. Only the last two — the updates of c and h — are -new ideas.

-

On screen: at first only the heading, the line "three gates and a candidate, -then two lines that update the states", and the picture underneath. The picture -is the same point drawn: one input (x, h), four boxes of the same shape — three -sigmoids giving the gates f, i and o, one tanh giving the candidate c~ — then -all four feed the update of c and h.

-

Reveal the equations in order and say what changes each time: the first four -are identical in form and differ only in their weights and their activation.

-

Click: the forget gate. Read it: "f at step t is the sigmoid of W_xf times x_t, -plus W_hf times the previous h, plus a bias." Point at the first sigma box.

-

Click 2: the input gate i_t — the same shape, its own weights.

-

Click 3: the candidate c~_t — the same shape again, but with tanh. Sigmoid for a -gate because a gate is a fraction in (0, 1); tanh for the candidate because -content should be signed.

-

Click 4: the output gate o_t, top of the right column — the fourth copy.

-

Click 5: the cell update. "c_t is f_t times the old c, plus i_t times the -candidate." Keep a fraction of the old memory, add a fraction of the new -content. The odot is elementwise, not a matrix product: every gate decision is -per unit.

-

Click 6: the output. "h_t is o_t times tanh of c_t" — squash the memory, then -let the output gate decide how much of it to show.

-

Click 7: the callout. Four of the six are the same layer, sigma or tanh of -Wx + Wh + b, each with its own weights. That is why the parameter count is -exactly 4x the plain RNN's, and why nn.LSTM stores one weight_ih of shape -(4H, input) rather than four matrices.

-

Every symbol is named on the next slide, "Reading the equation: the LSTM cell".

-`,images:[],raw:``,frontmatter:Gt,filepath:``,start:2680,sourceIndex:39,id:39,no:40},__clicksContext:null,__preloaded:!1}),qt=u({layout:`default`,title:`Reading the equation: the LSTM cell`}),Jt=d({get layout(){return qt.layout},get transition(){return qt.transition},get class(){return qt.class},get clicks(){return qt.clicks},get name(){return qt.name},get preload(){return qt.preload},slide:{content:``,revision:`f3dowj`,frontmatterRaw:`layout: default -title: "Reading the equation: the LSTM cell" -`,note:``,title:`Reading the equation: the LSTM cell`,index:40,noteHTML:`

Eight rows, because the six equations share most of their symbols. Group them as -you read: three gates, one candidate, two states, one input, one set of weights per -gate, two squashing functions, one new operator.

-

The subscript dot in W_x., W_h., b. stands for the gate's letter: W_xf, W_xi, -W_xc, W_xo and so on. Four input matrices, four recurrent matrices, four biases -(three gates plus the candidate), which is why the parameter count is 4x the plain -RNN's.

-

The worked example shows where the next slide's numbers come from. The next -slide starts from f = 0.9, i = 0.4, c~ = 0.8, o = 0.7 (the GatedCell widget's -defaults); this one shows that each is just sigma or tanh of a weighted sum. Do -the forget gate in full and only quote the other three pre-activations. Checked: -sigma(2.2) = 0.900, sigma(-0.4) = 0.401, tanh(1.1) = 0.800, sigma(0.85) = 0.701.

-

The odot row is the one students get wrong: it multiplies matching entries, unit -by unit. c_t = f_t odot c_{t-1} means "each unit keeps its own fraction of its own -memory".

-

Two letters clash with earlier slides: o_t here is the output GATE, not an output -of the plain RNN, and W_ho here is the gate's recurrent weight, not the output -layer. The literature uses the same letters for both; say it once.

-`,images:[],raw:``,frontmatter:qt,filepath:``,start:2800,sourceIndex:40,id:40,no:41},__clicksContext:null,__preloaded:!1}),Yt=u({layout:`default`,title:`One LSTM step, by hand`}),Xt=d({get layout(){return Yt.layout},get transition(){return Yt.transition},get class(){return Yt.class},get clicks(){return Yt.clicks},get name(){return Yt.name},get preload(){return Yt.preload},slide:{content:``,revision:`vx1z5e`,frontmatterRaw:`layout: default -title: One LSTM step, by hand -`,note:``,title:`One LSTM step, by hand`,index:41,noteHTML:`

Have them do it on paper while you click, then check against the GatedCell widget -("The LSTM memory block"): the numbers are the widget's defaults, so it works.

-

The four clicks, in order: -kept f x c_{t-1} = 0.9 x 0.6 = 0.54 -written i x c~ = 0.4 x 0.8 = 0.32 -cell c_t = 0.54 + 0.32 = 0.86 -shown h_t = o x tanh(c_t) = 0.7 x tanh(0.86) = 0.7 x 0.696 = 0.49 -(checked in Python: 0.4874.)

-

The distinction between what the cell holds and what it shows is the job of -the output gate, and it is the part of the LSTM that students most often cannot -explain. This slide is where it gets explained.

-

Running example, in words: if this unit held the not-flag, the "kept" line is -what carries it from word to word. With f near 1, almost all of it survives each -word. The output gate can keep it hidden until "great" arrives. Do not put numbers -on that: we have not set LSTM weights for the review.

-`,images:[],raw:``,frontmatter:Yt,filepath:``,start:2883,sourceIndex:41,id:41,no:42},__clicksContext:null,__preloaded:!1}),Zt=u({layout:`interactive`,heading:`The highway, against the chain`,title:`The highway, against the chain`,"aside-width":`20rem`}),Qt=d({get layout(){return Zt.layout},get transition(){return Zt.transition},get class(){return Zt.class},get clicks(){return Zt.clicks},get name(){return Zt.name},get preload(){return Zt.preload},slide:{content:``,revision:`-e5ewy3`,frontmatterRaw:`layout: interactive -heading: The highway, against the chain -title: The highway, against the chain -aside-width: 20rem -`,note:``,title:`The highway, against the chain`,index:42,noteHTML:`

THE PAYOFF. This is the slide the whole deck is built for: slow down.

-

Say out loud: it is not magic. A learned forget gate, held near 1, replaces the -fixed weight 0.8.

-

Ask the question first and let it sit. Then click: 0.012, the number from section -02 ("What vanishing feels like"), drawn as a bar on a 0-to-1 scale. It is a -sliver. Click: 0.82 on the same scale. Leave both on screen and say the two -numbers out loud, side by side.

-

Checked in Python: 0.8^20 = 0.0115 (the widget's readout says 0.0115; the slide -rounds to 0.012), 0.99^20 = 0.818. The bars are to scale: 190 px = 1.

-

The chart: red is the plain recurrence, multiplied by W = 0.8 per word back; teal -is the cell state, multiplied by f = 0.99. Then push f to 1.000 on the slider: -the factor is exactly 1 at any distance.

-

The secondary line is the honest version and it matters: LSTMs do not abolish -vanishing gradients. They make the decay rate a learned, per-unit, per-step -quantity. An LSTM whose forget gates all learn 0.5 vanishes as fast as anything -else.

-

This is also why forget-gate biases are often initialised to 1 or 2: it starts -the gate open, so the highway is clear before training has learned anything. -Worth saying for anyone who reads implementations.

-`,images:[],raw:``,frontmatter:Zt,filepath:``,start:2964,sourceIndex:42,id:42,no:43},__clicksContext:null,__preloaded:!1}),$t=u({layout:`default`,title:`Where the LSTM came from`}),en=d({get layout(){return $t.layout},get transition(){return $t.transition},get class(){return $t.class},get clicks(){return $t.clicks},get name(){return $t.name},get preload(){return $t.preload},slide:{content:``,revision:`-8n4yp9`,frontmatterRaw:`layout: default -title: Where the LSTM came from -`,note:``,title:`Where the LSTM came from`,index:43,noteHTML:`

Short and cited. The history is -worth one minute for one reason: the forget gate was not in the original design, -which is a useful antidote to reading an architecture as if every part were -inevitable.

-

1991: Hochreiter's diploma thesis already described the vanishing gradient. -1997: Hochreiter and Schmidhuber built the LSTM to fix it. At its centre is the -"constant error carousel", the 1997 paper's own name for the additive cell state. -It is a better name than "highway". -2000: Gers, Schmidhuber and Cummins added the forget gate. Without it the state -saturated on long streams: it filled up and stopped changing. Today it is the -most important gate of the three. -2014: the GRU, two slides on.

-

What came after the LSTM starts at the end of this lecture (attention, in its -2014 recurrent form) and continues in a later lecture; do not open it here.

-`,images:[],raw:``,frontmatter:$t,filepath:``,start:3027,sourceIndex:43,id:43,no:44},__clicksContext:null,__preloaded:!1}),tn=u({layout:`interactive`,heading:`The GRU — same idea, fewer parts`,title:`The GRU — same idea, fewer parts`,"aside-width":`20rem`}),nn=d({get layout(){return tn.layout},get transition(){return tn.transition},get class(){return tn.class},get clicks(){return tn.clicks},get name(){return tn.name},get preload(){return tn.preload},slide:{content:``,revision:`nbx2cf`,frontmatterRaw:`layout: interactive -heading: The GRU — same idea, fewer parts -title: The GRU — same idea, fewer parts -aside-width: 20rem -`,note:``,title:`The GRU — same idea, fewer parts`,index:44,noteHTML:`

The point: the GRU is the same gating idea with fewer parts — one state instead -of two, two gates instead of three — and it works about as well.

-

On screen: the GatedCell widget in its GRU variant: a slider for each gate, bars -for the old state, the candidate and the new state. The aside: the GRU (gated -recurrent unit) has one state and two gates.

-

Click: the update gate z does the forget and input jobs at once. One number -decides both how much to keep and how much to write.

-

Click 2: the reset gate r sets how much of the past the candidate may read. Drive -the reset gate to 0 now: the candidate bar jumps, because it has stopped reading -the state.

-

Click 3: the blend. Read it: "h_t is (1 - z) times the old h, plus z times the -candidate." The two shares sum to 1. Now drive z to 0 and 1 in turn: at 0 the -state is frozen, at 1 it is totally overwritten.

-

The structural consequence worth naming: an LSTM can forget without writing -(f = 0, i = 0) and a GRU cannot, because its kept and written shares are tied. -In practice this costs almost nothing, and the GRU has a quarter fewer weights -(three weight sets instead of four).

-

Cho et al., 2014. The way the blend is written here (z = how much to update) -follows Chung et al., 2014; the next slide shows why that matters.

-`,images:[],raw:``,frontmatter:tn,filepath:``,start:3082,sourceIndex:44,id:44,no:45},__clicksContext:null,__preloaded:!1}),rn=u({layout:`default`,title:`Which one, and the sign that will catch you`}),an=d({get layout(){return rn.layout},get transition(){return rn.transition},get class(){return rn.class},get clicks(){return rn.clicks},get name(){return rn.name},get preload(){return rn.preload},slide:{content:``,revision:`d41ge4`,frontmatterRaw:`layout: default -title: Which one, and the sign that will catch you -`,note:``,title:`Which one, and the sign that will catch you`,index:45,noteHTML:`

The bars are the parameter count to scale: one weight set for the plain RNN -(64x128 + 128x128 + 2x128 = 24 832, with PyTorch's two bias vectors), three for -the GRU (74 496), four for the LSTM (99 328). Checked in Python.

-

The callout: benchmarks have argued about LSTM versus GRU since 2014 and the -answer is "it depends, by about a point". If pressed: GRU trains faster and is -usually equal on small data; LSTM has a small edge on very long dependencies and -on language modelling. Neither difference is worth an afternoon.

-

The sign trap is the thing they will actually hit, and only if they check an -implementation against a paper, which is what a good student does. These slides -(and the GatedCell widget) write h_t = (1 - z) h_{t-1} + z h~, as Chung et al. -(2014) and many textbooks do. Cho et al. (2014), the GRU paper itself, write -h_t = z h_{t-1} + (1 - z) h~, and PyTorch's nn.GRU follows Cho: -h_t = (1 - z) * n_t + z * h_{t-1}. Same model; the gate means the opposite.

-

(An earlier version of this deck had this backwards, saying Cho used the update -convention. Fixed in the 2026-10-04 rework.)

-`,images:[],raw:``,frontmatter:rn,filepath:``,start:3132,sourceIndex:45,id:45,no:46},__clicksContext:null,__preloaded:!1}),on=u({layout:`default`}),sn=d({get layout(){return on.layout},get transition(){return on.transition},get class(){return on.class},get clicks(){return on.clicks},get name(){return on.name},get preload(){return on.preload},slide:{content:``,revision:`-pg6srq`,frontmatterRaw:`layout: default -`,note:``,index:46,noteHTML:`

Hands up for each. Option 4 is the popular wrong answer: tanh is applied on the -way out to h, never to c itself. That confusion is worth naming out loud.

-

Option 3 is the 1997 cell's real problem, but it came from input that kept -being added. With input = 0 nothing is added, so nothing fills up.

-

This is the sealed envelope with both gates shut: nothing gets in, nothing leaks -out. That is the whole reason the architecture exists.

-`,images:[],raw:``,frontmatter:on,filepath:``,start:3185,sourceIndex:46,id:46,no:47},__clicksContext:null,__preloaded:!1}),cn=u({layout:`section`,index:`04`}),ln=d({get layout(){return cn.layout},get transition(){return cn.transition},get class(){return cn.class},get clicks(){return cn.clicks},get name(){return cn.name},get preload(){return cn.preload},slide:{content:``,revision:`8te5ms`,frontmatterRaw:`layout: section -index: "04" -`,note:``,title:`Words into vectors`,level:1,index:47,noteHTML:`

Section 04 is compressible. If you are behind, show "An embedding is a lookup -table" only: our toy table was hand-set, a real one is learned, 64 numbers per -word. The lab covers the code.

-

The glyph: words as points on a plane. Similar words sit close together. It comes -back on the overview and the recap.

-`,images:[],raw:``,frontmatter:cn,filepath:``,start:3244,sourceIndex:47,id:47,no:48},__clicksContext:null,__preloaded:!1}),un=u({layout:`default`,title:`A word is not a number`}),dn=d({get layout(){return un.layout},get transition(){return un.transition},get class(){return un.class},get clicks(){return un.clicks},get name(){return un.name},get preload(){return un.preload},slide:{content:``,revision:`-302zw8`,frontmatterRaw:`layout: default -title: A word is not a number -`,note:``,title:`A word is not a number`,index:48,noteHTML:`

Left: index it. great -> 4 217. One number per word, and arithmetic on it is -nonsense. It says 4 217 is "more" than 2 103, and it puts great between whatever -words happen to be numbered 4 216 and 4 218. This is what a student's first -tokeniser produces, and it is the tensor they then feed straight into a Linear -layer by mistake.

-

Click: one-hot it, the usual answer. A 20 000-long vector, 19 999 zeros and one

-
    -
  1. Honest: no false order. But every pair of words is exactly sqrt(2) apart, so -great and brilliant are as different as great and aardvark. One-hot throws away -every relationship between words before the model sees anything. It is also what -made the dense layer cost a billion weights on "Try it with the network we -already have" in section 00.
    2. -
-

The callout sets up the next slide: our toy review already used two hand-set -numbers per word. The question is where real ones come from.

-`,images:[],raw:``,frontmatter:un,filepath:``,start:3264,sourceIndex:48,id:48,no:49},__clicksContext:null,__preloaded:!1}),fn=u({layout:`interactive`,heading:`An embedding is a lookup table`,title:`An embedding is a lookup table`,"aside-width":`19rem`}),pn=d({get layout(){return fn.layout},get transition(){return fn.transition},get class(){return fn.class},get clicks(){return fn.clicks},get name(){return fn.name},get preload(){return fn.preload},slide:{content:``,revision:`-rvb0ek`,frontmatterRaw:`layout: interactive -heading: An embedding is a lookup table -title: An embedding is a lookup table -aside-width: 19rem -`,note:``,title:`An embedding is a lookup table`,index:49,noteHTML:`

Start with the toy table in the aside: it is the one from the start of the lecture, two numbers -per word, "positive word" and "negation word", written by hand so every number -could be checked. Say plainly: that was for teaching. In a real model those rows -are weights, learned like any other, and there are 64 numbers per word, not 2.

-

Then the widget, and say it out loud: it is a different, bigger table, nine words -and four numbers each, chosen to look like a learned one. It does not contain our -"not" row.

-

Click a few words and let them watch the highlighted row move. The claim to make -unmissable: one-hot times a matrix IS row selection, so the "lookup table" and -the "linear layer" descriptions are the same object. E is V x d: V rows (the -vocabulary size), d columns (the embedding width), with d much smaller than V.

-

Which also answers "how does it learn?": the same way every other weight matrix -does. The gradient reaching E is non-zero only in the rows for words that were in -the batch, which is why embedding gradients are sparse.

-`,images:[],raw:``,frontmatter:fn,filepath:``,start:3323,sourceIndex:49,id:49,no:50},__clicksContext:null,__preloaded:!1}),mn=u({layout:`interactive`,heading:`What it learns is a geometry`,title:`What it learns is a geometry`,"aside-width":`21rem`}),hn=d({get layout(){return mn.layout},get transition(){return mn.transition},get class(){return mn.class},get clicks(){return mn.clicks},get name(){return mn.name},get preload(){return mn.preload},slide:{content:``,revision:`ohcl7u`,frontmatterRaw:`layout: interactive -heading: What it learns is a geometry -title: What it learns is a geometry -aside-width: 21rem -`,note:``,title:`What it learns is a geometry`,index:50,noteHTML:`

The point: an embedding table is not a list of arbitrary codes; after training, -words that are used alike sit near each other, and that geometry is what the -model has learned.

-

On screen: the EmbeddingLab widget in "space" mode — each word is a dot in two -dimensions. The aside opens with the key fact: in a trained model these -coordinates are weights, moved by gradient descent like any other.

-

Click: great and brilliant end up together. Point at the pair in the widget.

-

Click 2: awful and terrible too, at the far end of the same axis.

-

Click 3: the and a sit near zero on both axes — they carry neither sentiment nor -meaning about films.

-

Click 4: the honest caveat in the secondary line, and it matters. The widget's -axes, "sentiment" and "is a film noun", are hand-written to look like a trained -table; they are not the toy review's axes ("positive word", "negation word"), and -this is not the same table. A real table has 64 unnamed axes. Do not let anyone -leave thinking dimension 1 is "sentiment" in a real model.

-

The useful consequence: a word the model saw twice can inherit from a word it saw -a thousand times, because they sit near each other. That is what "extraction of -salient features" means when textbooks list it as a benefit.

-

For the running example: in a trained table, "not" would end up somewhere of its -own, away from the sentiment words, because what it does is flip them, not carry -a sentiment of its own. Say that as an expectation, not a measured fact.

-`,images:[],raw:``,frontmatter:mn,filepath:``,start:3379,sourceIndex:50,id:50,no:51},__clicksContext:null,__preloaded:!1}),gn=u({layout:`default`,title:`Embeddings in code, and where they come from`}),_n=d({get layout(){return gn.layout},get transition(){return gn.transition},get class(){return gn.class},get clicks(){return gn.clicks},get name(){return gn.name},get preload(){return gn.preload},slide:{content:``,revision:`nf4v06`,frontmatterRaw:`layout: default -title: Embeddings in code, and where they come from -`,note:``,title:`Embeddings in code, and where they come from`,index:51,noteHTML:`

The point: an embedding layer is a lookup table — integer ids in, one learned -row of 64 numbers per id out — and it is usually most of the model's weights.

-

On screen: the shape picture across the top. Token ids of shape (2, 5) go into -the table of 20 000 x 64 and come out as vectors of shape (2, 5, 64). Below, the -code that does it, with the two reviews as a batch.

-

Click: lines 1-2 — the table: 20 000 words, 64 numbers each. padding_idx is the -practical line. Row 0 is pinned at zero and gets no gradient. Without it the pad -row drifts during training and quietly contributes to every short sequence in -the batch.

-

Click 2: lines 4-5 — the two reviews as ids. Review B is the first row; review -A is one word shorter, so it is padded with id 0 to length 5. The ids are made up -(a real tokeniser assigns them); 4217 is "great", the same number as on "A word -is not a number".

-

Click 3: line 6 — the lookup. (B, T) in, (B, T, d) out: batch 2, time 5, width 64.

-

Click 4: line 7 — the table itself is a weight matrix of shape (20000, 64).

-

Click 5: the whole block again.

-

Click 6: integer ids in, shape (batch, time) — no one-hot vectors anywhere.

-

Click 7: padding_idx=0 keeps row 0 at zero, as on line 2.

-

Click 8: 1.28 M learned numbers, starting random. 20 000 x 64 = 1 280 000. Put an -LSTM(64, 128) and the Linear(128, 2) head on top and the embedding is 92.8% of -all the weights (1 280 000 of 1 379 586, the same classifier as section 05).

-

Click 9: the callout — or start from a table pre-trained on billions of words. -Pre-trained tables (word2vec 2013, GloVe 2014, fastText 2016) were the transfer -learning of language models from 2013 to 2018, the same idea as starting a -network from pre-trained weights. The words came pre-trained but the model -reading them did not.

-`,images:[],raw:``,frontmatter:gn,filepath:``,start:3438,sourceIndex:51,id:51,no:52},__clicksContext:null,__preloaded:!1}),vn=u({layout:`section`,index:`05`}),yn=d({get layout(){return vn.layout},get transition(){return vn.transition},get class(){return vn.class},get clicks(){return vn.clicks},get name(){return vn.name},get preload(){return vn.preload},slide:{content:``,revision:`rylktj`,frontmatterRaw:`layout: section -index: "05" -`,note:``,title:`An RNN in PyTorch`,level:1,index:52,noteHTML:`

Section 06 is compressible: the lab covers all of it. If you are behind, show -"Shapes, end to end" and "Four bugs, and what each one looks like", and send the -rest to the lab.

-

The glyph is a stack of slabs with three sizes marked, B, T and F: one padded -batch of reviews. It is the picture behind every slide in this section.

-`,images:[],raw:``,frontmatter:vn,filepath:``,start:3533,sourceIndex:52,id:52,no:53},__clicksContext:null,__preloaded:!1}),bn=u({layout:`default`,title:`The shape that causes the most bugs`}),xn=d({get layout(){return bn.layout},get transition(){return bn.transition},get class(){return bn.class},get clicks(){return bn.clicks},get name(){return bn.name},get preload(){return bn.preload},slide:{content:``,revision:`-26tb9h`,frontmatterRaw:`layout: default -title: The shape that causes the most bugs -`,note:``,title:`The shape that causes the most bugs`,index:53,noteHTML:`

This is the single most expensive line in the lecture. Say it, write it on the -board, and say it again when the code slide comes up.

-

Ask the question first, then click to the red row. The failure mode: a -(B, T, F) tensor fed to a batch_first=False layer is read as T = 32 steps of a -batch of 200 "reviews", each "review" being one word position across the batch. -The loss goes down — it is still learning something — and the accuracy is -nonsense.

-

Why the default is time-first at all: it is convenient for cuDNN, the NVIDIA GPU -library that runs the LSTM loop. Batch-first matches every other PyTorch layer, -and nn.Embedding's output.

-

The h_n exception catches people who then try to be consistent about it.

-`,images:[],raw:``,frontmatter:bn,filepath:``,start:3553,sourceIndex:53,id:53,no:54},__clicksContext:null,__preloaded:!1}),Sn=u({layout:`default`,title:`nn.LSTM returns two things, and you want one of them`}),Cn=d({get layout(){return Sn.layout},get transition(){return Sn.transition},get class(){return Sn.class},get clicks(){return Sn.clicks},get name(){return Sn.name},get preload(){return Sn.preload},slide:{content:``,revision:`cdjd7f`,frontmatterRaw:`layout: default -title: nn.LSTM returns two things, and you want one of them -`,note:``,title:`nn.LSTM returns two things, and you want one of them`,index:54,noteHTML:`

The point: nn.LSTM hands back two things, and which one you keep depends on the -task shape. Classifying a review needs one vector per review; tagging every word -needs one vector per word.

-

On screen: the code on the left, and on the right the same code as a picture — -five LSTM steps over review B, each with an output vector on top. output is all -five of them; h_n is the last one, drawn in the accent colour.

-

Click: the first two lines light up. One LSTM layer, 64 numbers in per word (the -embedding size), 128 numbers of state. batch_first=True means the batch is the -first axis, which is how our data loader hands it over.

-

Click 2: lines 4–5. A fake batch: 32 reviews, 200 steps, 64 numbers per step, so -(B, T, embed). The call returns output and a tuple (h_n, c_n).

-

Click 3: line 7. output is (32, 200, 128): the state at every one of the 200 -steps, for every review. That is the row of five vectors in the picture.

-

Click 4: line 8. h_n is (1, 32, 128): only the last step. The leading 1 is the -number of layers (times directions); with num_layers=2 it would be 2.

-

Click 5: the whole block again. Ask the room which of the two they would hand to -a classifier head before the bullets answer it.

-

Click 6: classify a review — take h_n[-1], shape (32, 128). The [-1] picks the -last layer, so it stays correct if you stack layers later.

-

Click 7: a label per word — take output, (32, 200, 128), and put a head on every -step.

-

Click 8: the two agree: output[:, -1] equals h_n[-1], as long as nothing is -padded. Have them check it as an assertion in the lab: -torch.allclose(output[:, -1, :], h_n[-1]). It is true, and the "when nothing is -padded" caveat is the whole subject of the next two slides.

-

Click 9: the callout. Pick by the task shape. This points back to "Which shapes -does a sequence problem come in?": classifying a review is many to one, a tag -per word is many to many. Pick the wrong one and the shapes can still line up -after a reshape, so nothing crashes — the model just learns the wrong thing.

-

c_n is the cell state and you almost never touch it — except to pass it back in -when continuing a sequence, as in truncated backpropagation through time.

-

nn.RNN and nn.GRU return (output, h_n): one tensor, not a tuple. Only the LSTM -hands back two states, because only the LSTM has two.

-`,images:[],raw:``,frontmatter:Sn,filepath:``,start:3617,sourceIndex:54,id:54,no:55},__clicksContext:null,__preloaded:!1}),wn=u({layout:`default`,title:`Sequences in a batch are not the same length`}),Tn=d({get layout(){return wn.layout},get transition(){return wn.transition},get class(){return wn.class},get clicks(){return wn.clicks},get name(){return wn.name},get preload(){return wn.preload},slide:{content:``,revision:`nkpnnl`,frontmatterRaw:`layout: default -title: Sequences in a batch are not the same length -`,note:``,title:`Sequences in a batch are not the same length`,index:55,noteHTML:`

The point: a batch is one rectangle, but reviews have different lengths. The -gaps get filled with a pad token, and unless the model is told the real lengths, -it reads the padding as if it were words.

-

On screen: four reviews as rows of word tiles, padded out to 8 slots. Faint -tiles are padding. The third row is the long one: our review B with three more -words ("but well acted"). Lengths are 5, 3, 8 and 2. In the tensor each tile is -an integer id; the pad id is 0 by convention, and nn.Embedding(padding_idx=0) -keeps its vector at zero.

-

Click: count it. 18 real tokens out of 32 slots, 14 pads — nearly half the batch -is nothing.

-

Click 2: draw attention to the last row, "utterly awful". The LSTM runs all 8 -steps on it: two real words, then six steps reading padding.

-

Click 3: so h_n is the state after six updates on a token that means nothing. -Nothing crashes; the model just gets a worse feature vector for every short -review in every batch. That is why the batch has to carry its lengths: a length -tensor is part of the batch, and the collate function has to build it.

-

This is the single most common reason a student's RNN scores worse than their -bag-of-words baseline.

-

If asked how to waste less: sorting a batch by length reduces the padding a lot. -Bucketing by length reduces it further.

-`,images:[],raw:``,frontmatter:wn,filepath:``,start:3727,sourceIndex:55,id:55,no:56},__clicksContext:null,__preloaded:!1}),En=u({layout:`default`,title:`Packing — tell the layer where to stop`}),Dn=d({get layout(){return En.layout},get transition(){return En.transition},get class(){return En.class},get clicks(){return En.clicks},get name(){return En.name},get preload(){return En.preload},slide:{content:``,revision:`-hlla1l`,frontmatterRaw:`layout: default -title: Packing — tell the layer where to stop -`,note:``,title:`Packing — tell the layer where to stop`,index:56,noteHTML:`

The point: packing tells the LSTM where each review really ends, so it stops -each row at its own length. Without it, h_n is the wrong vector for every padded -row.

-

On screen: the code on the left. On the right, the same four reviews sorted by -length, 8, 5, 3, 2 words, with one column per time step (numbered along the -top). The accent cell in each row is the last real token — where h_n is now -read; the red cells after it are padding.

-

The bottom row is the part people ask about. Read it column by column: under -each step, count the rows that still have a real (not red) token in that -column. -Steps 1 and 2: all four reviews have a word, so 4 and 4. -Step 3: the 2-word review ("utterly awful") has ended, so 3. -Steps 4 and 5: the 3-word review has ended too, so 2 and 2. -Steps 6, 7 and 8: only the 8-word review is left, so 1, 1, 1. -That row, 4, 4, 3, 2, 2, 1, 1, 1, is exactly what a PackedSequence stores in -its batch_sizes field. At each step the LSTM processes only that many rows, so -it never reads a pad token. The numbers add up to 18, the real tokens in the -batch. Sorting by length is what makes the rows still going always the top -ones.

-

Click: lines 1–4. Import the two helpers and pack the embedded batch with its -lengths. enforce_sorted=False is the modern convenience; older code sorts the -batch by length by hand and then has to unsort the outputs. Show them the flag -exists. lengths must be on the CPU — a real error message people hit and do not -expect.

-

Click 2: line 5. The LSTM takes the packed batch directly and skips the pad -steps. Nothing else about the call changes.

-

Click 3: lines 6–8. h_n[-1] is now the state after the last real token of each -row. output comes back as a PackedSequence; pad_packed_sequence turns it into a -padded tensor again, with zeros where the padding was.

-

Click 4: the whole block again — five lines of code, and only one of them is -the LSTM.

-

Click 5: the callout. This is not an optimisation; it is a correctness fix.

-

For a many-to-many model you could skip packing and mask the loss instead, and -that is a legitimate choice. For many-to-one you cannot: there is nothing to -mask, the damage is already inside h_n.

-`,images:[],raw:``,frontmatter:En,filepath:``,start:3785,sourceIndex:56,id:56,no:57},__clicksContext:null,__preloaded:!1}),On=u({layout:`default`,title:`The model`}),kn=d({get layout(){return On.layout},get transition(){return On.transition},get class(){return On.class},get clicks(){return On.clicks},get name(){return On.name},get preload(){return On.preload},slide:{content:``,revision:`35cveb`,frontmatterRaw:`layout: default -title: The model -`,note:``,title:`The model`,index:57,noteHTML:`

The point: the whole review classifier is three layers — embedding, LSTM, -linear — and the only new line compared with an MLP or CNN is the pack.

-

On screen: the model class on the left, and on the right a shape strip: ids -(32, 200), Embedding, emb (32, 200, 64), LSTM packed, h_n[-1] (32, 128) in the -accent colour, Linear, logits (32, 2). The 200 disappears at the LSTM. That is -where the sequence becomes a vector.

-

Click: lines 4–5. The embedding turns each id into 64 numbers; padding_idx=0 -keeps the pad token's vector at zero. The LSTM reads 64 and keeps 128 numbers of -state, batch first.

-

Click 2: lines 6–7. Dropout of 0.3, and a linear head to 2 logits, positive and -negative. The dropout sits on the final state, between the recurrence and the -head — the same place as in the CNN, for the same reason.

-

Click 3: line 10. Read the shape comment aloud: (B, T, 64). Every shape comment -on this slide is checkable and they should check them.

-

Click 4: lines 11–14. Pack with the lengths, run the LSTM, keep only h_n, -(1, B, 128). Take h_n[-1], dropout, head: (B, 2).

-

Click 5: the whole class again; point at the shape strip and walk down it once.

-

Click 6: the note underneath. Three layers, and the only new line is the pack. -h_n[-1] is the last layer's final state, so the code stays right with -num_layers=2.

-

Dropout inside the recurrence needs care: dropping a different set of units -every step destroys the state. nn.LSTM's own dropout= argument applies between -stacked layers, not across time, and it does nothing at all with num_layers=1 -(PyTorch warns about this).

-

No softmax, because CrossEntropyLoss applies it — the same trap as for CNNs.

-`,images:[],raw:``,frontmatter:On,filepath:``,start:3886,sourceIndex:57,id:57,no:58},__clicksContext:null,__preloaded:!1}),An=u({layout:`default`,title:`Shapes, end to end`}),jn=d({get layout(){return An.layout},get transition(){return An.transition},get class(){return An.class},get clicks(){return An.clicks},get name(){return An.name},get preload(){return An.preload},slide:{content:``,revision:`-y5dhbo`,frontmatterRaw:`layout: default -title: Shapes, end to end -`,note:``,title:`Shapes, end to end`,index:58,noteHTML:`

This is a shape ledger for a sequence model: write every tensor's shape down. -The strip on top is the same story in three boxes: the T axis of 200 disappears -at the LSTM.

-

The output row: with packed input, output is a PackedSequence, not a tensor. -pad_packed_sequence turns it back into (32, T, 128), where T is the longest -review in this batch — 200 only if some review fills the padding. This model -never uses output (it reads h_n), so the row is there to stop someone indexing -output[:, -1] and silently reading padding.

-

The check: -model = ReviewClassifier(20_000) -ids = torch.randint(1, 20_000, (32, 200)) -lengths = torch.randint(5, 201, (32,)) -print(model(ids, lengths).shape) # torch.Size([32, 2])

-

Four lines, and they catch every shape mistake on this slide.

-

Note which dimension is 200 and which is 32 at every row, and that 200 disappears -after h_n — that is where the sequence becomes a vector.

-`,images:[],raw:``,frontmatter:An,filepath:``,start:3980,sourceIndex:58,id:58,no:59},__clicksContext:null,__preloaded:!1}),Mn=u({layout:`default`,title:`The training loop, with one line added`}),Nn=d({get layout(){return Mn.layout},get transition(){return Mn.transition},get class(){return Mn.class},get clicks(){return Mn.clicks},get name(){return Mn.name},get preload(){return Mn.preload},slide:{content:``,revision:`kce5u0`,frontmatterRaw:`layout: default -title: The training loop, with one line added -`,note:``,title:`The training loop, with one line added`,index:59,noteHTML:`

The point: the training loop is the same one used for MLPs and CNNs. A new -architecture is a new nn.Module, plus one line: gradient clipping.

-

On screen: the loop, and under it a strip — forward, loss, backward, clip, step -— with clip in the accent colour and "gradient length ≤ 1.0" under it.

-

Click: lines 1–3. The model, CrossEntropyLoss, Adam with learning rate 1e-3. -Nothing here is specific to sequences.

-

Click 2: lines 6–7. model.train() and the loader. The loader now yields three -things: ids, lengths and labels. lengths stays on the CPU deliberately — -pack_padded_sequence requires it, and the model does the .cpu() itself, so this -loop does not have to care.

-

Click 3: lines 8–11. Move ids and labels to the device, forward with the -lengths, compute the loss, zero the gradients. Familiar.

-

Click 4: lines 12–14. backward, then the clip, then step. The clip line is the -only addition ("Gradient clipping, in one line" in section 03), and it is the one -that keeps a recurrent run from dying on its first long batch. It sits between -backward() and step(), which the strip shows: it rescales the whole gradient so -its length is at most 1.0, after the gradients exist and before the weights -move.

-

Click 5: the whole loop again. Ask: which line would you delete to make this a -CNN loop? Only the clip, and the lengths argument.

-

Click 6: the note. The MLP's loop, the CNN's loop and this one differ by one line -and one model class.

-`,images:[],raw:``,frontmatter:Mn,filepath:``,start:4048,sourceIndex:59,id:59,no:60},__clicksContext:null,__preloaded:!1}),Pn=u({layout:`default`,title:`Four bugs, and what each one looks like`}),Fn=d({get layout(){return Pn.layout},get transition(){return Pn.transition},get class(){return Pn.class},get clicks(){return Pn.clicks},get name(){return Pn.name},get preload(){return Pn.preload},slide:{content:``,revision:`ilk7j9`,frontmatterRaw:`layout: default -title: Four bugs, and what each one looks like -`,note:``,title:`Four bugs, and what each one looks like`,index:60,noteHTML:`

The point: this slide is the lab's FAQ, written in advance. Four bugs everyone -makes, and three of them give no error message at all.

-

On screen: the bullets on the left reveal one bug per click. The plot on the -right is a sketch, not a measured run: the green curve is the correct model and -goes lowest; the grey "bug" curve is what any of the three silent bugs looks -like — it trains, it flattens out, just higher; the red curve is the run without -clipping, which stops at nan.

-

Click: batch_first left out. The LSTM defaults to (T, B, features), so the model -reads 32 reviews as 32 time steps and still trains.

-

Click 2: no packing. h_n is the state after the padding, so every short review -gets a worse feature vector.

-

Click 3: loss on padding. The slide says when: a label per word (many to many), -as in tagging or next-word prediction. Our review classifier has one label per -review, so its labels are never padding — this bug waits for the generation -model. nn.CrossEntropyLoss(ignore_index=0) is the one-argument fix, and it is -why the pad id is conventionally 0 and conventionally reserved. Without it the -model spends most of its capacity predicting the pad token.

-

Click 4: no clipping. Point at the red curve. Once a nan reaches the weights, -nothing recovers — every later forward pass is nan. If a run goes to nan, -restart it; do not wait.

-

Click 5: the callout. Three of the four give no error message: the loss still -falls, just not far enough. The habit that catches all three silent ones: print -your shapes, and check h_n against lengths.

-`,images:[],raw:``,frontmatter:Pn,filepath:``,start:4125,sourceIndex:60,id:60,no:61},__clicksContext:null,__preloaded:!1}),In=u({layout:`section`,index:`06`}),Ln=d({get layout(){return In.layout},get transition(){return In.transition},get class(){return In.class},get clicks(){return In.clicks},get name(){return In.name},get preload(){return In.preload},slide:{content:``,revision:`-3r5ql6`,frontmatterRaw:`layout: section -index: "06" -`,note:``,title:`Generating, and the wall`,level:1,index:61,noteHTML:`

Section 06. The first slide closes the loop with the window slide in section 00; -the rest is the encoder–decoder, attention in its 2014 form, and the one problem attention does -not fix. Compressible after the generation slide if time is short: show -"One sequence in, another out", "The other wall" and the recap.

-

The glyph: a cell writes one tile, and the tile curls back in as the next input.

-`,images:[],raw:``,frontmatter:In,filepath:``,start:4201,sourceIndex:61,id:61,no:62},__clicksContext:null,__preloaded:!1}),Rn=u({layout:`default`,title:`Generating one step at a time`}),zn=d({get layout(){return Rn.layout},get transition(){return Rn.transition},get class(){return Rn.class},get clicks(){return Rn.clicks},get name(){return Rn.name},get preload(){return Rn.preload},slide:{content:``,revision:`b4j5gd`,frontmatterRaw:`layout: default -title: Generating one step at a time -`,note:``,title:`Generating one step at a time`,index:62,noteHTML:`

The point: an RNN that predicts the next word is used in two different ways. -At training time it is fed the true review; at generation time it is fed its -own guesses. This closes the loop with the window slide in section 00: predict -the next word from the ones before it, with the state in place of the window.

-

On screen, left panel (training). Read it bottom to top. The bottom row is the -input at each step: the true words of review B, "the movie was not". The middle -row is the same RNN cell at four steps, with the state passed along to the -right. The top row is the target: the true next word at each step, "movie was -not great". The model's prediction at each step is compared with that target, -which gives one cross-entropy loss per step (the "loss" labels). The four -losses are averaged into the loss for the review.

-

Click: the right panel (generating). Now there is no true review; we want the -model to write one. We give it only the first word, "the" (the accent tile). It -predicts a next word, here "movie". The dashed curve shows that guess fed back -in as the input of the next step. Then it predicts "was", feeds it back, and so -on. Each step must wait for the guess of the step before, so generation runs -one word at a time.

-

Click 2: training uses the true previous word as input. This has a name, -teacher forcing: like a teacher who corrects you after every word, so a mistake -at step 2 does not affect the input of step 3. Because every input is known -before we start, nn.LSTM processes the whole review in one forward call and -computes the loss for every step at once.

-

Click 3: generating uses the model's own last guess. The model never saw its -own mistakes during training, so one wrong word puts it in a situation it was -not trained on, and the error carries into every later step. That mismatch -between training and generating is called exposure bias, and it is why long -generated text drifts. Scheduled sampling, which mixes in the model's own -predictions during training with rising probability, is the classic fix.

-

How the next word is chosen, if the room asks: the output at each step is a -softmax over the vocabulary. Always taking the most likely word (argmax) is -repetitive; sampling from softmax(logits / temperature) is the usual knob.

-`,images:[],raw:``,frontmatter:Rn,filepath:``,start:4219,sourceIndex:62,id:62,no:63},__clicksContext:null,__preloaded:!1}),Bn=u({layout:`interactive`,heading:`One sequence in, another out`,title:`One sequence in, another out`,"aside-width":`21rem`}),Vn=d({get layout(){return Bn.layout},get transition(){return Bn.transition},get class(){return Bn.class},get clicks(){return Bn.clicks},get name(){return Bn.name},get preload(){return Bn.preload},slide:{content:``,revision:`-ie1sc4`,frontmatterRaw:`layout: interactive -heading: One sequence in, another out -title: One sequence in, another out -aside-width: 21rem -`,note:``,title:`One sequence in, another out`,index:63,noteHTML:`

The point: an encoder–decoder squeezes the whole source sentence into one -context vector of fixed size. That size does not grow with the sentence, and -that is the bottleneck.

-

On screen: the widget. Bottom row: the encoder, an RNN that reads the German -source sentence. Top row: the decoder, a second RNN that writes the English -target one word at a time, exactly as on the previous slide — but starting from -the encoder's final state instead of from zero. In the middle, the context box: -256 numbers. The slider sets how many words are in the source, from 2 to 8.

-

Before the clicks: read the aside's first line. Lengths and word order differ -between the languages, and nothing is written until the whole source is read.

-

Click: the encoder's final state, the context vector, is all the decoder knows -about the source.

-

Click 2: drag the slider from 2 to 8 slowly and say the number out loud each -time: still 256. That is the whole slide. The readout also says how many more -updates the first word has to survive before the encoder is done.

-

Click 3: the callout. Eight words into 256 numbers is generous. Forty into the -same 256 is the bottleneck.

-

The second half of the argument, which the picture cannot show: the first word of -the source has to survive every later update of the encoder state before the -decoder sees anything. Long sources lose their beginnings — the vanishing note -from section 03 again.

-

Cho et al. measured it in 2014 — translation quality (BLEU score) falls off -sharply past about 20 source words. The fix was published the same year.

-`,images:[],raw:``,frontmatter:Bn,filepath:``,start:4317,sourceIndex:63,id:63,no:64},__clicksContext:null,__preloaded:!1}),Hn=u({layout:`interactive`,heading:`Attention — stop choosing in advance`,title:`Attention — stop choosing in advance`,"aside-width":`20rem`}),Un=d({get layout(){return Hn.layout},get transition(){return Hn.transition},get class(){return Hn.class},get clicks(){return Hn.clicks},get name(){return Hn.name},get preload(){return Hn.preload},slide:{content:``,revision:`-qr7qwu`,frontmatterRaw:`layout: interactive -heading: Attention — stop choosing in advance -title: Attention — stop choosing in advance -aside-width: 20rem -`,note:``,title:`Attention — stop choosing in advance`,index:64,noteHTML:`

The point: attention drops the single fixed summary. The decoder keeps every -encoder state and builds a fresh context for each word it writes.

-

On screen: the same widget in attention mode, on "Ich habe ein Buch gelesen" → -"I have read a book". Each press of Next word writes one English word; the -weights over the five German encoder states appear above them, and the readout -says which German word gets most of the weight.

-

Press Next word once: writing "I", the weight is 0.90 on "Ich". Then press again -for "have" before the first click.

-

Click: "have" — the weight sits on "habe", 0.85.

-

Press Next word to reach "read".

-

Click 2: "read" — 0.85 of the weight on "gelesen", the last German word. Stop -here. The German verb is at the end of the clause and the English one is in the -middle; the attention lines cross, and the crossing is the evidence that the -model is not just copying word order.

-

Click 3: the alignment is learned, not given. Nobody told the model that "read" -goes with "gelesen". Press on through "a" (0.88 on "ein") and "book" (0.86 on -"Buch") if there is time.

-

Click 4: the callout. No fixed summary to overflow: the decoder looks words up -instead of remembering them.

-

This is Bahdanau, Cho & Bengio, 2014 — attention added to a recurrent -encoder–decoder. The recurrence is still there: the encoder is an RNN and the -decoder is an RNN. Attention only changes what the decoder reads at each step.

-

Do not spend the mathematics here — the next slide is three lines.

-`,images:[],raw:``,frontmatter:Hn,filepath:``,start:4378,sourceIndex:64,id:64,no:65},__clicksContext:null,__preloaded:!1}),Wn=u({layout:`default`,title:`Attention, in three lines`}),Gn=d({get layout(){return Wn.layout},get transition(){return Wn.transition},get class(){return Wn.class},get clicks(){return Wn.clicks},get name(){return Wn.name},get preload(){return Wn.preload},slide:{content:``,revision:`-53jfz8`,frontmatterRaw:`layout: default -title: Attention, in three lines -`,note:``,title:`Attention, in three lines`,index:65,noteHTML:`

The point: attention is three lines of maths — score, softmax, weighted sum — -and all three are differentiable, so the alignment is learned like any other -weight.

-

On screen: the heading says it in words — a new context at every output step, a -weighted average of encoder states. The equations reveal one at a time on the -left. The picture on the right is the worked example from the next slide, drawn -as the three stages: scores 1.1, 0, 0; softmax weights 0.6, 0.2, 0.2 as bars; -the three states feeding one context vector [0.6, 0.6], with line widths -proportional to the weights.

-

Click: the score. "e t i equals a of s t minus 1 and h i": for output step t, -score each encoder state h_i against the decoder's state before writing, -s_{t-1}. The score function a is a small MLP (multi-layer perceptron) in the 2014 -paper; later papers often use a plain dot product, which is what the worked -example on the next slide does.

-

Click 2: the softmax. "alpha t i is e to the score, divided by the sum of e to -every score." The weights are positive and add up to 1 across the source words. -In the picture: 1.1, 0, 0 become 0.6, 0.2, 0.2.

-

Click 3: the weighted sum. "c t is the sum over i of alpha t i times h i." The -context for this step is mostly h_1, a little of the others.

-

Click 4: the callout — the one thing to point at. Everything on this slide is -differentiable, so the alignment is learned by the same gradient descent as the -rest. Nobody supplies alignments.

-

Three lines, then stop. Do not go further than this today. "The other wall" names -the problem attention does not fix.

-`,images:[],raw:``,frontmatter:Wn,filepath:``,start:4442,sourceIndex:65,id:65,no:66},__clicksContext:null,__preloaded:!1}),Kn=u({layout:`default`,title:`Reading the equation: attention`}),qn=d({get layout(){return Kn.layout},get transition(){return Kn.transition},get class(){return Kn.class},get clicks(){return Kn.clicks},get name(){return Kn.name},get preload(){return Kn.preload},slide:{content:``,revision:`72s4gl`,frontmatterRaw:`layout: default -title: "Reading the equation: attention" -`,note:``,title:`Reading the equation: attention`,index:66,noteHTML:`

Three source words, two-number states, so it fits on paper. The decoder state -s_{t-1} = [1.1, 0] points the same way as h_1, so h_1 gets the high score.

-

Score: dot products s . h_i = 1.1, 0, 0. -Softmax: exp gives 3.0, 1, 1 (e^1.1 = 3.004); total 5; weights 0.6, 0.2, 0.2. -Weighted sum: 0.6 [1, 0] + 0.2 [0, 1] + 0.2 [0, 2] = [0.6, 0.6]. -(Exactly: 0.6003, 0.1998, 0.1998, and c = [0.600, 0.600].)

-

Connect it to the widget two slides back: writing "read", the weight on -"gelesen" was 0.85. That number is one alpha_{t,i}, computed exactly like this.

-

Two clashes of letters to name out loud. c_t here is the context vector, not the -LSTM's cell state — the 2014 paper and the LSTM paper both chose c. And s is the -decoder's hidden state; it is called s only so it is not confused with the -encoder's h.

-

The index j only appears inside the sum in the softmax: it runs over all source -positions, so the denominator is the same for every i. That is why the weights -add up to 1.

-`,images:[],raw:``,frontmatter:Kn,filepath:``,start:4558,sourceIndex:66,id:66,no:67},__clicksContext:null,__preloaded:!1}),X=u({layout:`default`,title:`The other wall — and this one attention does not fix`}),Jn=d({get layout(){return X.layout},get transition(){return X.transition},get class(){return X.class},get clicks(){return X.clicks},get name(){return X.name},get preload(){return X.preload},slide:{content:``,revision:`ltjrla`,frontmatterRaw:`layout: default -title: The other wall — and this one attention does not fix -`,note:``,title:`The other wall — and this one attention does not fix`,index:67,noteHTML:`

The point: there is a second wall, and attention does not fix it. h_t needs -h_{t-1}, so the steps of a recurrent network cannot run in parallel. Spend two -minutes; this slide explains why recurrent models were set aside.

-

On screen: against clock time. The top row is the RNN: one state per tick, h1 to -h5, five ticks for five words, because h2 needs h1. The bottom row is a -convolution, or an attention weighted sum on its own: every position computed in -the first tick — the stacked boxes, "all at once".

-

Click: 200 tokens means 200 matrix multiplies, one after another.

-

Click 2: the batch runs in parallel — 32 reviews at once is fine. Time cannot: -step 7 of every review waits for step 6.

-

Click 3: more hardware does not shorten the chain. A bigger GPU makes each step -faster, not the number of steps smaller.

-

Click 4: the callout. Attention fixed the bottleneck, but the encoder and decoder -are still RNNs: one step at a time.

-

Both walls matter, and they are different: the bottleneck is about what the model -can represent, and attention fixed it in 2014. Parallelism is about what a GPU can -train, and it is the reason recurrent models were abandoned rather than improved. -Scale needed parallelism, and recurrence cannot provide it.

-

The 2017 paper that removed the recurrence and kept only the attention is called -"Attention Is All You Need". This wall is why that paper matters. Name the wall -and stop: every step waits for the one before.

-

If someone asks what recurrent models are still good for: very long or streaming -sequences, small on-device models, and the state-space revival — Mamba and -similar models are recurrent networks with a computation that can run in -parallel during training.

-`,images:[],raw:``,frontmatter:X,filepath:``,start:4640,sourceIndex:67,id:67,no:68},__clicksContext:null,__preloaded:!1}),Yn=u({layout:`default`,title:`Where we got to`}),Xn=d({get layout(){return Yn.layout},get transition(){return Yn.transition},get class(){return Yn.class},get clicks(){return Yn.clicks},get name(){return Yn.name},get preload(){return Yn.preload},slide:{content:``,revision:`ezrics`,frontmatterRaw:`layout: default -title: Where we got to -`,note:``,title:`Where we got to`,index:68,noteHTML:`

One glyph per section, in the order of the lecture. Point at each one and ask the -room for its sentence before you click it in.

-

Sequence: order matters and lengths vary, so keep one state and carry it forward. -Loop: one cell, its weights shared across every step. -Fade: the gradient back to "not" 20 steps away is scaled by 0.8^20 = 0.012. -Gate: the LSTM's forget gate of 0.99 keeps 0.99^20 = 0.82 of it. -Map: the 2-number word vectors were a toy; real ones are learned, 64 numbers or -more. -Shape: batch_first=True, pack the padded batch, clip the gradient. -Generate: feed the output back in to write text; let the decoder look back at -every encoder state with attention.

-

The last callout names both walls. The first is fixed; the second, steps that -must run one after another, is still open at the end of today.

-`,images:[],raw:``,frontmatter:Yn,filepath:``,start:4727,sourceIndex:68,id:68,no:69},__clicksContext:null,__preloaded:!1}),Zn=u({layout:`default`,title:`Reading, and the lab`}),Qn=d({get layout(){return Zn.layout},get transition(){return Zn.transition},get class(){return Zn.class},get clicks(){return Zn.clicks},get name(){return Zn.name},get preload(){return Zn.preload},slide:{content:``,revision:`-4oan5o`,frontmatterRaw:`layout: default -title: Reading, and the lab -`,note:``,title:`Reading, and the lab`,index:69,noteHTML:`

The point: three things to read, and the lab — where the bugs from this lecture -get made on purpose.

-

On screen: three link cards, then the lab description with a small picture on -the right: three runs, each with one thing removed — no batch_first, no packing, -no clipping — and each run's accuracy a question mark to measure.

-

Click: Olah, "Understanding LSTMs" (2015). The clearest explanation of the -gates; read it once before the lab.

-

Click 2: Karpathy, "The Unreasonable Effectiveness of RNNs" (2015). A -character-level LSTM writing Shakespeare, C code and LaTeX — the generation -slide, at scale.

-

Click 3: the nn.LSTM documentation. Worth reading for the shapes, the two bias -vectors, and the GRU gate written the other way round.

-

Click 4: the lab. Train the review classifier, then break it on purpose, one -change at a time, and report what each change costs. That report is the graded -part. The deliberate-breakage exercise is the point. A student who has watched -batch_first quietly cost them accuracy never forgets it, and no amount of -saying it from the front achieves that.

-

The three runs on the right are three of the bugs from "Four bugs, and what each -one looks like": two silent ones, and clipping, the loud one. Each run changes -exactly one thing against a working baseline.

-

Practicalities — dataset, deadline, what to hand in — belong on the course page.

-`,images:[],raw:``,frontmatter:Zn,filepath:``,start:4790,sourceIndex:69,id:69,no:70},__clicksContext:null,__preloaded:!1}),$n=u({layout:`end`,email:`vajira@simula.no`}),er=d({get layout(){return $n.layout},get transition(){return $n.transition},get class(){return $n.class},get clicks(){return $n.clicks},get name(){return $n.name},get preload(){return $n.preload},slide:{content:``,revision:`-w7qfsi`,frontmatterRaw:`layout: end -email: vajira@simula.no -`,note:``,title:`To be continued…`,level:1,index:70,noteHTML:`

The running example, one last time: "the movie was not great", the not-flag -switching on at "not", the sentiment going negative at "great", and P(positive) -= 0.22. Two numbers of state carried that across the review.

-

Leave time for questions. A likely one: "if attention is so good, why keep the -RNN at all?" — answer it with the second wall: the attention still sits inside -an RNN, so every step still waits for the one before.

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100`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`,`),i(`span`,{style:{"--shiki-dark":`#BD976A`,"--shiki-light":`#B07D48`}},` device`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`=`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`dev`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`))`),i(`span`,{style:{"--shiki-dark":`#758575DD`,"--shiki-light":`#A0ADA0`}},` # steps 1–3`)]),c(` -`),i(`span`,{class:`line`},[i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},` loss_d `),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`=`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},` bce`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`(`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`D`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`(`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`real`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`),`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},` ones`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`)`),i(`span`,{style:{"--shiki-dark":`#CB7676`,"--shiki-light":`#AB5959`}},` +`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},` 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opt_d`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`.`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`zero_grad`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`()`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`; loss_d`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`.`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`backward`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`()`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`; opt_d`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`.`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`step`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`()`)]),c(` -`),i(`span`,{class:`line`}),c(` 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G`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`(`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`torch`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`.`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`randn`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`(`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`real`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`.`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`size`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`(`),i(`span`,{style:{"--shiki-dark":`#4C9A91`,"--shiki-light":`#2F798A`}},`0`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`),`),i(`span`,{style:{"--shiki-dark":`#4C9A91`,"--shiki-light":`#2F798A`}},` 100`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`,`),i(`span`,{style:{"--shiki-dark":`#BD976A`,"--shiki-light":`#B07D48`}},` device`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`=`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`dev`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`))`),i(`span`,{style:{"--shiki-dark":`#758575DD`,"--shiki-light":`#A0ADA0`}},` # step 4`)]),c(` -`),i(`span`,{class:`line`},[i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},` loss_g `),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`=`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},` bce`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`(`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`D`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`(`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},`fake`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`),`),i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},` ones`),i(`span`,{style:{"--shiki-dark":`#666666`,"--shiki-light":`#999999`}},`)`),i(`span`,{style:{"--shiki-dark":`#758575DD`,"--shiki-light":`#A0ADA0`}},` # label 1 — the non-saturating form`)]),c(` -`),i(`span`,{class:`line`},[i(`span`,{style:{"--shiki-dark":`#DBD7CAEE`,"--shiki-light":`#393A34`}},` 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Zg=`__floating-vue__popper`,Qg=()=>di({name:`VPopper`,provide(){return{[Zg]:{parentPopper:this}}},inject:{[Zg]:{default:null}},props:{theme:{type:String,required:!0},targetNodes:{type:Function,required:!0},referenceNode:{type:Function,default:null},popperNode:{type:Function,required:!0},shown:{type:Boolean,default:!1},showGroup:{type:String,default:null},ariaId:{default:null},disabled:{type:Boolean,default:$(`disabled`)},positioningDisabled:{type:Boolean,default:$(`positioningDisabled`)},placement:{type:String,default:$(`placement`),validator:e=>Vg.includes(e)},delay:{type:[String,Number,Object],default:$(`delay`)},distance:{type:[Number,String],default:$(`distance`)},skidding:{type:[Number,String],default:$(`skidding`)},triggers:{type:Array,default:$(`triggers`)},showTriggers:{type:[Array,Function],default:$(`showTriggers`)},hideTriggers:{type:[Array,Function],default:$(`hideTriggers`)},popperTriggers:{type:Array,default:$(`popperTriggers`)},popperShowTriggers:{type:[Array,Function],default:$(`popperShowTriggers`)},popperHideTriggers:{type:[Array,Function],default:$(`popperHideTriggers`)},container:{type:[String,Object,Xg,Boolean],default:$(`container`)},boundary:{type:[String,Xg],default:$(`boundary`)},strategy:{type:String,validator:e=>[`absolute`,`fixed`].includes(e),default:$(`strategy`)},autoHide:{type:[Boolean,Function],default:$(`autoHide`)},handleResize:{type:Boolean,default:$(`handleResize`)},instantMove:{type:Boolean,default:$(`instantMove`)},eagerMount:{type:Boolean,default:$(`eagerMount`)},popperClass:{type:[String,Array,Object],default:$(`popperClass`)},computeTransformOrigin:{type:Boolean,default:$(`computeTransformOrigin`)},autoMinSize:{type:Boolean,default:$(`autoMinSize`)},autoSize:{type:[Boolean,String],default:$(`autoSize`)},autoMaxSize:{type:Boolean,default:$(`autoMaxSize`)},autoBoundaryMaxSize:{type:Boolean,default:$(`autoBoundaryMaxSize`)},preventOverflow:{type:Boolean,default:$(`preventOverflow`)},overflowPadding:{type:[Number,String],default:$(`overflowPadding`)},arrowPadding:{type:[Number,String],default:$(`arrowPadding`)},arrowOverflow:{type:Boolean,default:$(`arrowOverflow`)},flip:{type:Boolean,default:$(`flip`)},shift:{type:Boolean,default:$(`shift`)},shiftCrossAxis:{type:Boolean,default:$(`shiftCrossAxis`)},noAutoFocus:{type:Boolean,default:$(`noAutoFocus`)},disposeTimeout:{type:Number,default:$(`disposeTimeout`)}},emits:{show:()=>!0,hide:()=>!0,"update:shown":e=>!0,"apply-show":()=>!0,"apply-hide":()=>!0,"close-group":()=>!0,"close-directive":()=>!0,"auto-hide":()=>!0,resize:()=>!0},data(){return{isShown:!1,isMounted:!1,skipTransition:!1,classes:{showFrom:!1,showTo:!1,hideFrom:!1,hideTo:!0},result:{x:0,y:0,placement:``,strategy:this.strategy,arrow:{x:0,y:0,centerOffset:0},transformOrigin:null},randomId:`popper_${[Math.random(),Date.now()].map(e=>e.toString(36).substring(2,10)).join(`_`)}`,shownChildren:new 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Use `autoSize="min"` instead.'),this.autoMaxSize&&console.warn("[floating-vue] `autoMaxSize` option is deprecated. Use `autoBoundaryMaxSize` instead.")},mounted(){this.init(),this.$_detachPopperNode()},activated(){this.$_autoShowHide()},deactivated(){this.hide()},beforeUnmount(){this.dispose()},methods:{show({event:e=null,skipDelay:t=!1,force:n=!1}={}){var r;(r=this.parentPopper)!=null&&r.lockedChild&&this.parentPopper.lockedChild!==this||(this.pendingHide=!1,(n||!this.disabled)&&(this.parentPopper?.lockedChild===this&&(this.parentPopper.lockedChild=null),this.$_scheduleShow(e,t),this.$emit(`show`),this.$_showFrameLocked=!0,requestAnimationFrame(()=>{this.$_showFrameLocked=!1})),this.$emit(`update:shown`,!0))},hide({event:e=null,skipDelay:t=!1}={}){if(!this.$_hideInProgress){if(this.shownChildren.size>0){this.pendingHide=!0;return}if(this.hasPopperShowTriggerHover&&this.$_isAimingPopper()){this.parentPopper&&(this.parentPopper.lockedChild=this,clearTimeout(this.parentPopper.lockedChildTimer),this.parentPopper.lockedChildTimer=setTimeout(()=>{this.parentPopper.lockedChild===this&&(this.parentPopper.lockedChild.hide({skipDelay:t}),this.parentPopper.lockedChild=null)},1e3));return}this.parentPopper?.lockedChild===this&&(this.parentPopper.lockedChild=null),this.pendingHide=!1,this.$_scheduleHide(e,t),this.$emit(`hide`),this.$emit(`update:shown`,!1)}},init(){this.isDisposed&&(this.isDisposed=!1,this.isMounted=!1,this.$_events=[],this.$_preventShow=!1,this.$_referenceNode=this.referenceNode?.call(this)??this.$el,this.$_targetNodes=this.targetNodes().filter(e=>e.nodeType===e.ELEMENT_NODE),this.$_popperNode=this.popperNode(),this.$_innerNode=this.$_popperNode.querySelector(`.v-popper__inner`),this.$_arrowNode=this.$_popperNode.querySelector(`.v-popper__arrow-container`),this.$_swapTargetAttrs(`title`,`data-original-title`),this.$_detachPopperNode(),this.triggers.length&&this.$_addEventListeners(),this.shown&&this.show())},dispose(){this.isDisposed||(this.isDisposed=!0,this.$_removeEventListeners(),this.hide({skipDelay:!0}),this.$_detachPopperNode(),this.isMounted=!1,this.isShown=!1,this.$_updateParentShownChildren(!1),this.$_swapTargetAttrs(`data-original-title`,`title`))},async onResize(){this.isShown&&(await this.$_computePosition(),this.$emit(`resize`))},async $_computePosition(){if(this.isDisposed||this.positioningDisabled)return;let e={strategy:this.strategy,middleware:[]};(this.distance||this.skidding)&&e.middleware.push(Zh({mainAxis:this.distance,crossAxis:this.skidding}));let t=this.placement.startsWith(`auto`);if(t?e.middleware.push(qh({alignment:this.placement.split(`-`)[1]??``})):e.placement=this.placement,this.preventOverflow&&(this.shift&&e.middleware.push(Qh({padding:this.overflowPadding,boundary:this.boundary,crossAxis:this.shiftCrossAxis})),!t&&this.flip&&e.middleware.push(Jh({padding:this.overflowPadding,boundary:this.boundary}))),e.middleware.push(Gh({element:this.$_arrowNode,padding:this.arrowPadding})),this.arrowOverflow&&e.middleware.push({name:`arrowOverflow`,fn:({placement:e,rects:t,middlewareData:n})=>{let r,{centerOffset:i}=n.arrow;return r=e.startsWith(`top`)||e.startsWith(`bottom`)?Math.abs(i)>t.reference.width/2:Math.abs(i)>t.reference.height/2,{data:{overflow:r}}}}),this.autoMinSize||this.autoSize){let t=this.autoSize?this.autoSize:this.autoMinSize?`min`:null;e.middleware.push({name:`autoSize`,fn:({rects:e,placement:n,middlewareData:r})=>{var i;if((i=r.autoSize)!=null&&i.skip)return{};let a,o;return n.startsWith(`top`)||n.startsWith(`bottom`)?a=e.reference.width:o=e.reference.height,this.$_innerNode.style[t===`min`?`minWidth`:t===`max`?`maxWidth`:`width`]=a==null?null:`${a}px`,this.$_innerNode.style[t===`min`?`minHeight`:t===`max`?`maxHeight`:`height`]=o==null?null:`${o}px`,{data:{skip:!0},reset:{rects:!0}}}})}(this.autoMaxSize||this.autoBoundaryMaxSize)&&(this.$_innerNode.style.maxWidth=null,this.$_innerNode.style.maxHeight=null,e.middleware.push($h({boundary:this.boundary,padding:this.overflowPadding,apply:({availableWidth:e,availableHeight:t})=>{this.$_innerNode.style.maxWidth=e==null?null:`${e}px`,this.$_innerNode.style.maxHeight=t==null?null:`${t}px`}})));let n=await Ng(this.$_referenceNode,this.$_popperNode,e);Object.assign(this.result,{x:n.x,y:n.y,placement:n.placement,strategy:n.strategy,arrow:{...n.middlewareData.arrow,...n.middlewareData.arrowOverflow}})},$_scheduleShow(e,t=!1){if(this.$_updateParentShownChildren(!0),this.$_hideInProgress=!1,clearTimeout(this.$_scheduleTimer),qg&&this.instantMove&&qg.instantMove&&qg!==this.parentPopper){qg.$_applyHide(!0),this.$_applyShow(!0);return}t?this.$_applyShow():this.$_scheduleTimer=setTimeout(this.$_applyShow.bind(this),this.$_computeDelay(`show`))},$_scheduleHide(e,t=!1){if(this.shownChildren.size>0){this.pendingHide=!0;return}this.$_updateParentShownChildren(!1),this.$_hideInProgress=!0,clearTimeout(this.$_scheduleTimer),this.isShown&&(qg=this),t?this.$_applyHide():this.$_scheduleTimer=setTimeout(this.$_applyHide.bind(this),this.$_computeDelay(`hide`))},$_computeDelay(e){let t=this.delay;return parseInt(t&&t[e]||t||0)},async $_applyShow(e=!1){clearTimeout(this.$_disposeTimer),clearTimeout(this.$_scheduleTimer),this.skipTransition=e,!this.isShown&&(this.$_ensureTeleport(),await Gg(),await this.$_computePosition(),await this.$_applyShowEffect(),this.positioningDisabled||this.$_registerEventListeners([...Dg(this.$_referenceNode),...Dg(this.$_popperNode)],`scroll`,()=>{this.$_computePosition()}))},async $_applyShowEffect(){if(this.$_hideInProgress)return;if(this.computeTransformOrigin){let e=this.$_referenceNode.getBoundingClientRect(),t=this.$_popperNode.querySelector(`.v-popper__wrapper`),n=t.parentNode.getBoundingClientRect(),r=e.x+e.width/2-(n.left+t.offsetLeft),i=e.y+e.height/2-(n.top+t.offsetTop);this.result.transformOrigin=`${r}px ${i}px`}this.isShown=!0,this.$_applyAttrsToTarget({"aria-describedby":this.popperId,"data-popper-shown":``});let e=this.showGroup;if(e){let t;for(let n=0;n0){this.pendingHide=!0,this.$_hideInProgress=!1;return}if(clearTimeout(this.$_scheduleTimer),!this.isShown)return;this.skipTransition=e,Wg(Kg,this),Kg.length===0&&document.body.classList.remove(`v-popper--some-open`);for(let e of Rg(this.theme)){let t=Yg(e);Wg(t,this),t.length===0&&document.body.classList.remove(`v-popper--some-open--${e}`)}qg===this&&(qg=null),this.isShown=!1,this.$_applyAttrsToTarget({"aria-describedby":void 0,"data-popper-shown":void 0}),clearTimeout(this.$_disposeTimer);let t=this.disposeTimeout;t!==null&&(this.$_disposeTimer=setTimeout(()=>{this.$_popperNode&&(this.$_detachPopperNode(),this.isMounted=!1)},t)),this.$_removeEventListeners(`scroll`),this.$emit(`apply-hide`),this.classes.showFrom=!1,this.classes.showTo=!1,this.classes.hideFrom=!0,this.classes.hideTo=!1,await Gg(),this.classes.hideFrom=!1,this.classes.hideTo=!0},$_autoShowHide(){this.shown?this.show():this.hide()},$_ensureTeleport(){if(this.isDisposed)return;let e=this.container;if(typeof e==`string`?e=window.document.querySelector(e):e===!1&&(e=this.$_targetNodes[0].parentNode),!e)throw Error(`No container for popover: `+this.container);e.appendChild(this.$_popperNode),this.isMounted=!0},$_addEventListeners(){let e=e=>{this.isShown&&!this.$_hideInProgress||(e.usedByTooltip=!0,!this.$_preventShow&&this.show({event:e}))};this.$_registerTriggerListeners(this.$_targetNodes,Hg,this.triggers,this.showTriggers,e),this.$_registerTriggerListeners([this.$_popperNode],Hg,this.popperTriggers,this.popperShowTriggers,e);let t=e=>{e.usedByTooltip||this.hide({event:e})};this.$_registerTriggerListeners(this.$_targetNodes,Ug,this.triggers,this.hideTriggers,t),this.$_registerTriggerListeners([this.$_popperNode],Ug,this.popperTriggers,this.popperHideTriggers,t)},$_registerEventListeners(e,t,n){this.$_events.push({targetNodes:e,eventType:t,handler:n}),e.forEach(e=>e.addEventListener(t,n,zg?{passive:!0}:void 0))},$_registerTriggerListeners(e,t,n,r,i){let a=n;r!=null&&(a=typeof r==`function`?r(a):r),a.forEach(n=>{let r=t[n];r&&this.$_registerEventListeners(e,r,i)})},$_removeEventListeners(e){let t=[];this.$_events.forEach(n=>{let{targetNodes:r,eventType:i,handler:a}=n;!e||e===i?r.forEach(e=>e.removeEventListener(i,a)):t.push(n)}),this.$_events=t},$_refreshListeners(){this.isDisposed||(this.$_removeEventListeners(),this.$_addEventListeners())},$_handleGlobalClose(e,t=!1){this.$_showFrameLocked||(this.hide({event:e}),e.closePopover?this.$emit(`close-directive`):this.$emit(`auto-hide`),t&&(this.$_preventShow=!0,setTimeout(()=>{this.$_preventShow=!1},300)))},$_detachPopperNode(){this.$_popperNode.parentNode&&this.$_popperNode.parentNode.removeChild(this.$_popperNode)},$_swapTargetAttrs(e,t){for(let n of this.$_targetNodes){let r=n.getAttribute(e);r&&(n.removeAttribute(e),n.setAttribute(t,r))}},$_applyAttrsToTarget(e){for(let t of this.$_targetNodes)for(let n in e){let r=e[n];r==null?t.removeAttribute(n):t.setAttribute(n,r)}},$_updateParentShownChildren(e){let t=this.parentPopper;for(;t;)e?t.shownChildren.add(this.randomId):(t.shownChildren.delete(this.randomId),t.pendingHide&&t.hide()),t=t.parentPopper},$_isAimingPopper(){let e=this.$_referenceNode.getBoundingClientRect();if(s_>=e.left&&s_<=e.right&&c_>=e.top&&c_<=e.bottom){let e=this.$_popperNode.getBoundingClientRect(),t=s_-a_,n=c_-o_,r=e.left+e.width/2-a_+(e.top+e.height/2)-o_+e.width+e.height,i=a_+t*r,a=o_+n*r;return l_(a_,o_,i,a,e.left,e.top,e.left,e.bottom)||l_(a_,o_,i,a,e.left,e.top,e.right,e.top)||l_(a_,o_,i,a,e.right,e.top,e.right,e.bottom)||l_(a_,o_,i,a,e.left,e.bottom,e.right,e.bottom)}return!1}},render(){return this.$slots.default(this.slotData)}});if(typeof document<`u`&&typeof window<`u`){if(Bg){let e=!zg||{passive:!0,capture:!0};document.addEventListener(`touchstart`,e=>$g(e,!0),e),document.addEventListener(`touchend`,e=>e_(e,!0),e)}else window.addEventListener(`mousedown`,e=>$g(e,!1),!0),window.addEventListener(`click`,e=>e_(e,!1),!0);window.addEventListener(`resize`,i_)}function $g(e,t){if(Fg.autoHideOnMousedown)t_(e,t);else for(let t=0;t=0;r--){let i=Kg[r];try{let r=i.containsGlobalTarget=i.mouseDownContains||i.popperNode().contains(e.target);i.pendingHide=!1,requestAnimationFrame(()=>{if(i.pendingHide=!1,!n[i.randomId]&&n_(i,r,e)){if(i.$_handleGlobalClose(e,t),!e.closeAllPopover&&e.closePopover&&r){let e=i.parentPopper;for(;e;)n[e.randomId]=!0,e=e.parentPopper;return}let a=i.parentPopper;for(;a&&n_(a,a.containsGlobalTarget,e);)a.$_handleGlobalClose(e,t),a=a.parentPopper}})}catch{}}}function n_(e,t,n){return n.closeAllPopover||n.closePopover&&t||r_(e,n)&&!t}function r_(e,t){if(typeof e.autoHide==`function`){let n=e.autoHide(t);return e.lastAutoHide=n,n}return e.autoHide}function i_(){for(let e=0;e{a_=s_,o_=c_,s_=e.clientX,c_=e.clientY},zg?{passive:!0}:void 0);function l_(e,t,n,r,i,a,o,s){let c=((o-i)*(t-a)-(s-a)*(e-i))/((s-a)*(n-e)-(o-i)*(r-t)),l=((n-e)*(t-a)-(r-t)*(e-i))/((s-a)*(n-e)-(o-i)*(r-t));return c>=0&&c<=1&&l>=0&&l<=1}var u_={extends:Qg()},d_=(e,t)=>{let n=e.__vccOpts||e;for(let[e,r]of t)n[e]=r;return n};function f_(e,t,n,r,i,a){return vs(),Cs(`div`,{ref:`reference`,class:_e([`v-popper`,{"v-popper--shown":e.slotData.isShown}])},[Ca(e.$slots,`default`,ve(Ms(e.slotData)))],2)}var p_=d_(u_,[[`render`,f_]]);function m_(){var e=window.navigator.userAgent,t=e.indexOf(`MSIE `);if(t>0)return parseInt(e.substring(t+5,e.indexOf(`.`,t)),10);if(e.indexOf(`Trident/`)>0){var n=e.indexOf(`rv:`);return parseInt(e.substring(n+3,e.indexOf(`.`,n)),10)}var r=e.indexOf(`Edge/`);return r>0?parseInt(e.substring(r+5,e.indexOf(`.`,r)),10):-1}var h_;function g_(){g_.init||(g_.init=!0,h_=m_()!==-1)}var __={name:`ResizeObserver`,props:{emitOnMount:{type:Boolean,default:!1},ignoreWidth:{type:Boolean,default:!1},ignoreHeight:{type:Boolean,default:!1}},emits:[`notify`],mounted(){g_(),ir(()=>{this._w=this.$el.offsetWidth,this._h=this.$el.offsetHeight,this.emitOnMount&&this.emitSize()});let e=document.createElement(`object`);this._resizeObject=e,e.setAttribute(`aria-hidden`,`true`),e.setAttribute(`tabindex`,-1),e.onload=this.addResizeHandlers,e.type=`text/html`,h_&&this.$el.appendChild(e),e.data=`about:blank`,h_||this.$el.appendChild(e)},beforeUnmount(){this.removeResizeHandlers()},methods:{compareAndNotify(){(!this.ignoreWidth&&this._w!==this.$el.offsetWidth||!this.ignoreHeight&&this._h!==this.$el.offsetHeight)&&(this._w=this.$el.offsetWidth,this._h=this.$el.offsetHeight,this.emitSize())},emitSize(){this.$emit(`notify`,{width:this._w,height:this._h})},addResizeHandlers(){this._resizeObject.contentDocument.defaultView.addEventListener(`resize`,this.compareAndNotify),this.compareAndNotify()},removeResizeHandlers(){this._resizeObject&&this._resizeObject.onload&&(!h_&&this._resizeObject.contentDocument&&this._resizeObject.contentDocument.defaultView.removeEventListener(`resize`,this.compareAndNotify),this.$el.removeChild(this._resizeObject),this._resizeObject.onload=null,this._resizeObject=null)}}},v_=br(`data-v-b329ee4c`);vr(`data-v-b329ee4c`);var y_={class:`resize-observer`,tabindex:`-1`};yr(),__.render=v_((e,t,n,r,i,a)=>(vs(),ws(`div`,y_))),__.__scopeId=`data-v-b329ee4c`,__.__file=`src/components/ResizeObserver.vue`;var b_=(e=`theme`)=>({computed:{themeClass(){return Lg(this[e])}}}),x_=di({name:`VPopperContent`,components:{ResizeObserver:__},mixins:[b_()],props:{popperId:String,theme:String,shown:Boolean,mounted:Boolean,skipTransition:Boolean,autoHide:Boolean,handleResize:Boolean,classes:Object,result:Object},emits:[`hide`,`resize`],methods:{toPx(e){return e!=null&&!isNaN(e)?`${e}px`:null}}}),S_=[`id`,`aria-hidden`,`tabindex`,`data-popper-placement`],C_={ref:`inner`,class:`v-popper__inner`},w_=[As(`div`,{class:`v-popper__arrow-outer`},null,-1),As(`div`,{class:`v-popper__arrow-inner`},null,-1)];function T_(e,t,n,r,i,a){let o=ha(`ResizeObserver`);return 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a/lecture-07/assets/slidev/DrawingControls-7DRfiHec.js +++ /dev/null @@ -1 +0,0 @@ -import{n as e}from"../rolldown-runtime-hePW80VL.js";import{$ as t,H as n,S as r,St as i,V as a,_ as o,b as s,et as c,l,m as u,r as d,u as f,ut as p,v as m,vt as h,w as g,xt as _,y as v,yt as y,z as b}from"../modules/shiki-CkJq9tSf.js";import{d as x,g as S}from"../modules/vue-Dz2ojcKF.js";import{t as C}from"../useDrawings-dMxKslDP.js";import{t as w}from"./IconButton-CfFb_8Iy.js";import{t as T}from"./VerticalDivider-E2da-Zpk.js";var E=g({__name:`Draggable`,props:{storageKey:{},initial:{}},setup(e){let t=e,r=p(null),i=t.initial??{x:0,y:0},a=t.storageKey?S(t.storageKey,i):p(i),{style:o}=x(r,{initialValue:a});return(e,t)=>(b(),s(`div`,{ref_key:`el`,ref:r,class:`fixed`,style:_(h(o))},[n(e.$slots,`default`)],4))}}),D={class:`flex bg-main p-2`},O={class:`inline-block w-7 text-center`},k={class:`pt-.5`},A={class:`i-carbon:pin-filled transform 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a/lecture-07/assets/slidev/VerticalDivider-E2da-Zpk.js +++ /dev/null @@ -1 +0,0 @@ -import{b as e,z as t}from"../modules/shiki-CkJq9tSf.js";import{t as n}from"../_plugin-vue_export-helper-BDNMzG2s.js";var r={},i={class:`w-1px opacity-10 bg-current m-1 lg:m-2`};function a(n,r){return t(),e(`div`,i)}var o=n(r,[[`render`,a]]);export{o as t}; \ No newline at end of file diff --git a/lecture-07/assets/slidev/context-DnYiQGy6.js b/lecture-07/assets/slidev/context-DnYiQGy6.js deleted file mode 100644 index ad118088fde5d081c6c52f2bbf0b73b66771aa9f..0000000000000000000000000000000000000000 --- a/lecture-07/assets/slidev/context-DnYiQGy6.js +++ /dev/null @@ -1 +0,0 @@ -import{g as e,ht as t,ut as n}from"../modules/shiki-CkJq9tSf.js";import{A as r,M as i}from"../modules/vue-Dz2ojcKF.js";var 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+0,0 @@ -import{$ as e,S as t,St as n,X as r,_ as i,b as a,g as o,m as s,ut as c,vt as l,w as u,x as d,xt as f,z as p}from"../modules/shiki-CkJq9tSf.js";import{$ as m,W as h,t as g,u as _}from"../useNav-DOs7c9Ie.js";import{g as v}from"../modules/vue-Dz2ojcKF.js";import{h as y,o as b}from"../index-DYEUBAok.js";import{t as x}from"./IconButton-CfFb_8Iy.js";import{t as S}from"./Modal-Bum_LA_c.js";import{n as C,t as w}from"./NoteDisplay-0sQOScYJ.js";import{r as T,t as E}from"./TimerBar-CppjXUvc.js";var D={class:`flex my-1`},O={class:`h-full flex flex-col`},k={class:`flex-none border-t border-main`,px3:``,py2:``},A={class:`flex-none border-t border-main`},j={class:`flex gap-1 items-center px-6 py-3`},M={key:0,class:`i-carbon:minimize`},N={key:1,class:`i-carbon:maximize`},P={class:`px2 my-auto`},F={class:`text-lg`},I={class:`opacity-50 text-sm`},L=u({__name:`notes`,setup(u){y({title:`Notes - 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