codeparrot/github-jupyter · Datasets at Hugging Face

repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
WillenZh/deep-learning-project	tutorials/autoencoder/Convolutional_Autoencoder.ipynb	54	92975	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional Autoencoder\n", "\n", "Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data." ] }, { "cell_type": "code", "exe...	mit
google-research/language	language/multiberts/coref.ipynb	1	985078	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "O1t22v2ReiTx" }, "source": [ "# Application: Gender Bias in Coreference Systems\n", "\n", "This notebook walks through the analysis in Section 4 of [the paper](https://openreview.net/pdf?id=K0E_F0gFDgA). We'll look at accuracy a...	apache-2.0
gigjozsa/HI_analysis_course	chapter_00_preface/00_references_and_further_reading.ipynb	4	1722	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**\n", "\n", " [Content](../chapter_00_preface/00_00_introduction.ipynb#preface:sec:content)\n", "* [Glossary](../chapter_00_preface/00_01_glossary.ipynb#preface:sec:glossary)\n", "* [0. Preface](00_00_introduction.ip...	gpl-2.0
JanetMatsen/meta4	analysis/plot/150104_heat_maps_on_hyak.ipynb	2	13108	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import os\n", "import re\n", "imp...	bsd-3-clause
akloster/porekit-python	examples/squiggle_classifier_1/Read_Until_Efficiency.ipynb	1	129567	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Theoretical Efficiency of Read Until Enrichment\n", "\n", "The \"Read Until\" feature of the Oxford Nanopore sequencing technology means a program can see the data coming in at each pore and, dependend on that data, reject th...	isc
Ledoux/ShareYourSystem	Pythonlogy/draft/Directer/PreReadme.ipynb	1	9824	{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Directer\n\n##Doc\n----\n\n\n> \n> The Directer is a walker through the folders of the harddrive, \n> assuring a call of _DirectingCallbackFunction at each level.\n> \n> \n\n----\n\n<small>\nV...	mit
logmonster/odt_webapp	docs/chp4_eCommerce/fix_odt_jeymart_user_trx.ipynb	1	12010251	null	apache-2.0
cwharland/data-science-from-scratch	Working With Data.ipynb	2	146960	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from __future__ import division\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import math\n"...	mit
kurige/notebooks	notebooks/Test.ipynb	1	657631	{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": true, "input": [ "R = rand(500,500)\n", "R' * R" ], "language": "python", "metadata": {}, "outputs": [ ...	mit
david4096/bioapi-examples	python_notebooks/1kg_sequence_annotation_service.ipynb	1	12098	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## GA4GH 1000 Genomes Sequence Annotations Example\n", "\n", "This example illustrates how to access the sequence annotations for a given set of ...." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ...	apache-2.0
mwidner/WebArchiveTextTools	src/Text Mining with Web Archives.ipynb	2	4307	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cécile Alduy and I have been working with a team of undergraduate RAs and with some members of the Stanford University Libraries staff to ...	gpl-2.0
Joshuaalbert/IonoTomo	src/ionotomo/notebooks/UVWFrame.ipynb	1	16098	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import (absolute_import, unicode_literals, division,\n", " print_function)\n", "import numpy as np\n", "\n", "i...	apache-2.0
thushear/MLInAction	tensorflow/convolutional_tensor_net.ipynb	1	14550	{ "cells": [ { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 倒库\n", "import tensorflow as tf\n", "import numpy as np\n", "from tensorflow.examples.tutorials.mnist import input_data" ] }, { "cell_type": "cod...	apache-2.0
NeuroDataDesign/seelviz	Tony/docs_ipynb_links/Testing+Structure+Tensor.ipynb	1	14818	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using tractography function in pipeline to generate structure tensors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the main analysis module and tractography module. Use the analysis module...	apache-2.0
george1328/george1328.github.io	notebooks/regression/regression.ipynb	2	266913	{ "metadata": { "name": "", "signature": "sha256:b194c82f3b6d32a265a5b1170ecf38dd5d244f69b616c9cb46fd31ada4ebfbdc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Regression using SciKit Le...	apache-2.0
psychemedia/ou-robotics-vrep	robotVM/notebooks/Demo - Square 2 - Variables.ipynb	1	8721	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Traverse a Square - Part 2 - Variables\n", "\n", "In this notebook, we will introduce one of the most powerful ideas in programming: the variable.\n", "\n", "A variable is a container that we can reference by name...	apache-2.0
ES-DOC/esdoc-jupyterhub	notebooks/messy-consortium/cmip6/models/emac-2-53-aerchem/aerosol.ipynb	1	84330	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "MIP Era: CMIP6 \n", "Institute: MESSY-CONSORTIUM \n", "Source ID: EMAC-2-53-AERCHEM \n", ...	gpl-3.0
Kulbear/deep-learning-nano-foundation	DLND-tv-script-generation/dlnd_tv_script_generation.ipynb	1	46901	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# TV Script Generation\n", "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simps...	mit
Tahsin-Mayeesha/udacity-mlnd-deeplearning-capstone	Notebooks/Baseline.ipynb	1	9717	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_...	mit
locie/locie_notebook	base_python/itertools-fr.ipynb	1	676	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Itérer avec classe avec itertools !" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "l...	lgpl-3.0
JorisBolsens/PYNQ	Pynq-Z1/notebooks/examples/video_filters.ipynb	2	867931	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Software Grayscale and Sobel filters on HDMI input\n", "\n", "This example notebook will demonstrate two image filters using a snapshot from the HDMI input: <br>\n", "1. Fir...	bsd-3-clause
ES-DOC/esdoc-jupyterhub	notebooks/inm/cmip6/models/inm-cm4-8/seaice.ipynb	1	99801	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "MIP Era: CMIP6 \n", "Institute: INM \n", "Source ID: INM-CM4-8 \n", "**Topic...	gpl-3.0
sailuh/perceive	Parsers/SecLists/Reply-Parse.ipynb	1	51865	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Seclists reply parse\n", "\n", "__Example: http://seclists.org/fulldisclosure/2017/Jan/0__\n", "\n", "With each reply, we'll attempt to parse out the following:\n", "* raw reply text, without html tags\n", " ...	gpl-2.0
james-prior/cohpy	20161216-dojo-list-versus-square-brackets.ipynb	1	5613	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'hello'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ ...	mit
rajathkumarmp/BinPy	BinPy/examples/notebook/ic/Series_7400/IC7430.ipynb	5	9446	{ "metadata": { "name": "", "signature": "sha256:7836c307211109fc0572a6007bfdcf683b081cce2759a0937677c98f07048209" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Usage of IC 7430" ] ...	bsd-3-clause
ocefpaf/secoora	notebooks/timeSeries/ssv/00-velocity_secoora.ipynb	2	10421	{ "metadata": { "kernelspec": { "codemirror_mode": { "name": "ipython", "version": 3 }, "display_name": "Iris (Python 2)", "language": "python", "name": "iris_python2" }, "name": "", "signature": "sha256:8958e88f73c0aaf001a82e539ac46040ab581cc5b06d4bc954f2deb08c593345" }, "nbformat": 3...	mit
loganjt/DropletJumpWedge	Repos_Data/ExitTime_Optimization.ipynb	2	6559	{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, glob, numpy as np, csv, math\n", "import numpy.ma as ma\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as colors\n", "impo...	gpl-3.0
GoogleCloudPlatform/cloudml-samples	notebooks/tensorflow/census/estimator/trainer/task.ipynb	1	9100	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2016 Google LLC \n", " \n", " Licensed under the Apache License, Version 2.0 (the \"License\"); \n", " you may not use this file except in compliance with the License. \n", " You may obtain a copy of the L...	apache-2.0
xR86/ml-stuff	kaggle/enron-email/Initial.ipynb	1	7305933	null	mit
poppy-project/community-notebooks	tutorials-education/poppy-humanoid_poppy-torso__vrep_installation et prise en main/poppy réel/Construction/construction & installation (poppy réel).ipynb	2	9747	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"png/poppy.png\" HEIGHT=200 WIDTH=200 ALIGN=right>\n", "<img src=\"png/inria.jpg\" HEIGHT=150 WIDTH=325 ALIGN=left >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"png/diagram...	lgpl-3.0
dsiufl/2015-Fall-Hadoop	instructor-notes/3-pyspark-wordcount.ipynb	2	5478	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spark version of wordcount examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prepare the pyspark environment." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { ...	mit
chbehrens/pr_bc_connectivity-1	CBCX_ON_CBC_contact_comparison.ipynb	2	42504	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparision between the size of basal contacts of CBCX and OFF-CBCs" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as ...	gpl-3.0
nkmk/python-snippets	notebook/argument_expand_dict.ipynb	1	4427	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def func(arg1, arg2, arg3):\n", " print('arg1 =', arg1)\n", " print('arg2 =', arg2)\n", " print('arg3 =', arg3)" ] }, { "cell_type": "code", "execution_count": 2,...	mit
agile-geoscience/welly	docs/_userguide/Projects.ipynb	1	99093	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Projects\n", "\n", "Wells are one of the fundamental objects in welly.\n", "\n", "Well objects include collections of Curve objects. Multiple Well objects can be stored in a Project.\n", "\n", "On this page, w...	apache-2.0
huilyu2/DataVisualization	project-spring2017/part2/Part2-ilnkage-version 2.ipynb	1	87421	{ "cells": [ { "cell_type": "code", "execution_count": 212, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import plotly \n", "plotly.tools.set_credentials_file(username='yuxiangling0809', api_key='vJxzgz9EZWkJZdHur9A8')" ] }, { "cell_type": "code", "executi...	mit
mne-tools/mne-tools.github.io	dev/_downloads/00e78bba5d10188fcf003ef05e32a6f7/decoding_time_generalization_conditions.ipynb	1	5309	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Decoding s...	bsd-3-clause
bryantbiggs/movie_torrents	notebooks/numbers_scrape_validate.ipynb	2	54312	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "sys.path.append('../src')\n", "\n", "from numbers_scraper import NumbersScraper\n", "SCRAPER = NumbersScraper()" ] }, { "cell_ty...	mit
h-mayorquin/time_series_basic	presentations/2016-01-21(Wall-Street-Letter-Latency-Prediction).ipynb	1	126378	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction of text with Nexa Letter Latency.\n", "This notbook is for seeing how much the delay between the code vector and the code is related to the accuaracy of the prediciton." ] }, { "cell_type": "code", "execut...	bsd-3-clause
tschinz/iPython_Workspace	02_WP/Wavedrom/example.ipynb	1	2431	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "import wavedrom\n", "\n", "a = {'signal': [\n", " {'name': 'clk', 'wave': 'p.....\|...'},\n", " {'n...	gpl-2.0
GoogleCloudPlatform/training-data-analyst	courses/machine_learning/feateng/feateng.ipynb	1	25179	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1> Feature Engineering </h1>\n", "\n", "In this notebook, you will learn how to incorporate feature engineering into your pipeline.\n", "<ul>\n", "<li> Working with feature columns </li>\n", "<li> Adding feature c...	apache-2.0
CalPolyPat/phys202-2015-work	days/day07/Interact.ipynb	1	15480	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Interact" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `interact` function (`IPython.html.widgets.interact`) automatically creates a graphical user interface (GUI) for exploring code and ...	mit
crdietrich/sparklines	Pandas Sparklines Demo.ipynb	1	305328	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Sparklines in Pandas\n", "\n", "Sparklines are small unlabeled plots, used to visually convey an idea in a small space. This script creates sparklines in a Pandas DataFrame which can then be displayed inline in a Jupyter No...	mit
MPIBGC-TEE/CompartmentalSystems	notebooks/nonl_gcm_3p_many_params/means_and_sd_for_v0_and_v5.ipynb	1	8307545	null	mit
SiggyF/notebooks	soapexample.ipynb	1	41883	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Using domeintabellen webservice" ] }, { "cell_type": "code", "collapsed": false, "input"...	gpl-3.0
mit-eicu/eicu-code	notebooks/medication.ipynb	1	87315	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# medication\n", "\n", "The medications table reflects the active medication orders for patients. These are orders but do not necessarily reflect administration to the patient. For example, while existence of data in the infusi...	mit
noppanit/machine-learning	animated-graph/Animated Graphs.ipynb	1	50162	{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from moviepy.video.io.bindings import mplfig_to_npimage\n", ...	mit
tpin3694/tpin3694.github.io	scala/find_largest_key_or_value_in_a_map.ipynb	1	2495	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Title: Find Largest Key Or Value In A Map \n", "Slug: find_largest_key_or_value_in_a_map \n", "Summary: Find Largest Key Or Value In A Map Using Scala. \n", "Date: 20...	mit
opengeostat/pygslib	pygslib/Ipython_templates/backtr_raw.ipynb	1	76221	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Testing the back normalscore transformation\n", "========\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "\n", ...	mit
Leguark/GeMpy	legacy/Geothealler big function.ipynb	2	250862	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:18: DeprecationWarning: stack(*tensors) ...	mit
henrysky/astroNN	demo_tutorial/VAE/variational_autoencoder_demo.ipynb	1	951028	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Variational Autoencoder demo with 1D data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is [astroNN](https://github.com/henrysky/astroNN), please take a look if you are interested in astronomy...	mit
turbomanage/training-data-analyst	blogs/goes16/maria/hurricanes2017.ipynb	2	213981	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 2017 Hurricane Tracks\n", "\n", "Demonstrates how to plot all the North American hurricane tracks in 2017, starting from the BigQuery public dataset." ] }, { "cell_type": "code", "execution_count": null, "met...	apache-2.0
thatguyandy27/python-sandbox	l3/Lecture8 - Using Arrays and Scalars.ipynb	1	3793	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { ...	mit
Ledoux/ShareYourSystem	Pythonlogy/ShareYourSystem/Readme.ipynb	1	1990	{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": [ "#ShareYourSystem\n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": "\n<!--\nFrozenIsBool False\n-->\n\n##More Descriptions at...	mit
jorgehatccrma/pyGrFNN	notebooks/utils/Oscillator Regimes.ipynb	2	25139	{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib2tikz import save as tikz_save" ] }, ...	bsd-3-clause
adityaka/misc_scripts	python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/05_05/Final/.ipynb_checkpoints/Annotations-checkpoint.ipynb	1	4054	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Plot Annotations</h1>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "impor...	bsd-3-clause
probml/pyprobml	notebooks/misc/linreg_divorce_numpyro.ipynb	1	93188	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "linreg_divorce_numpyro.ipynb", "provenance": [], "toc_visible": true, "authorship_tag": "ABX9TyMAtUs/BjAM8bCfZRQ7MotY", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, ...	mit
saketkc/notebooks	python/coursera-BayesianML/04_mcmc_assignment.ipynb	1	355126	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mime...	bsd-2-clause
irockafe/revo_healthcare	notebooks/MTBLS17/exploratory/MTBLS17_uhplc_pos_classifer_no_retcor.ipynb	1	266082	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> After performing centwav at 15ppm, then grouping with bw=2. No retention correction applied </h2>\n", "Remember, this dataset is weird. It has replicates of all measurements. Make sure to split them before doing and ML stuff. ...	mit
karlstroetmann/Formal-Languages	Ply/Conflicts-Resolved.ipynb	1	8073	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "with open (\"../style.css\", \"r\") as file:\n", " css = file.read()\n", "HTML(css)" ] }, { "cell_type": "markdown", "meta...	gpl-2.0
reallyasi9/riddlers	lower-face-dice-game/lower_face_dice_game.ipynb	1	211567	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "def roll_me(N=100):\n", " rolls = 1 # Always have to roll at least once\n", " r1 = np.random.randint(1, N+1) # Note that randint...	gpl-3.0
wasit7/cs634	2017/week07/DQN.ipynb	1	22009	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2017-10-14 20:47:15,571] Making new env: FrozenLake-v0\n" ] } ], "source": [ "%matplotlib inline\n", "import matp...	bsd-2-clause
whitews/flow_rate_qc	flow_rate_ks.ipynb	1	4071165	null	bsd-3-clause
jagarzone6/cmos	notebooks/Interface using tkinter.ipynb	2	3250	{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tkinter import *\n", "from tkinter import ttk\n", "import math\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as p...	mit
jbwhit/WSP-312-Tips-and-Tricks	notebooks/02-Visualization-and-code-organization.ipynb	1	4397	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Notebo...	mit
davebshow/prelims	modularity-no_auth_patron.ipynb	1	42682	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import networkx as nx\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "plt.rcPara...	mit
spatchcock/monetary_economics_python	notebooks/.ipynb_checkpoints/scotland-checkpoint.ipynb	1	181895	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{lcl}\n", "Y = C + G \\hspace{1cm} & (1) \\\\\n", "T = \\theta Y \\hspace{1cm} & (2) \\\\\n"...	mit
marburg-open-courseware/gmoc	docs/mpg-if_error_continue/notebooks/ggplot.ipynb	1	304517	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from ggplot import *\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "?ggplot" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}...	mit
mliu49/RMG-stuff	Kinetics/TestFamilies.ipynb	1	4535	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import rmgpy\n", "from rmgpy.data.rmg import RMGDatabase\n", "from rmgpy.rmg.react import *\n", "from rmgpy.reaction import Reaction\n", "from rmgpy.m...	mit
caganze/wisps	notebooks/.ipynb_checkpoints/y standards-checkpoint.ipynb	1	90770	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/users/caganze/research/splat\n", "\n", "\n", "Welcome to the Spex Prism Library Analysis Toolkit (SPLAT)!\n", "I...	mit
willettk/insight	notebooks/neural_networks_and_deep_learning.ipynb	1	26612	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Work with http://neuralnetworksanddeeplearning.com/" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from ma...	apache-2.0
nagordon/mechpy	tutorials/Composite_Plate_Mechanics_with_Python_Theory.ipynb	1	47227	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "- - - -\n", "# Mechpy Tutorials\n", "a mechanical engineering toolbox\n", "\n", "source code - https://github.com/nagordon/mechpy \n", "documentation - https://nagordon.github.io/mechpy/web/ \n", "\n", "- ...	mit
lyftzeigen/MachineLearningLessons	KerasFFNWeatherPrediction/KerasFFNWeatherPrediction.ipynb	1	154741	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Keras FFN weather prediction in Rostov-on-Don" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using ...	mit
paulovn/ml-vm-notebook	vmfiles/IPNB/Examples/d Scala/01 Hello world.ipynb	1	2989	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hello World in a Scala Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is a first try with a Scala (Spark) kernel. Please be patient on execution of the first cell, it takes a ...	bsd-3-clause
brandon-rhodes/pycon-pandas-tutorial	All.ipynb	1	318960	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "idx = pd.IndexSlice" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collaps...	mit
pligor/predicting-future-product-prices	04_time_series_prediction/24_price_history_seq2seq-full_dataset_testing.ipynb	2	100225	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -- coding: UTF-8 --\n", "#%load_ext autoreload\n", "%reload_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count":...	agpl-3.0
versae/DH2304	class06.ipynb	1	269729	{ "metadata": { "name": "", "signature": "sha256:67d77d14b77e2ab47288c369cce432a58ccc63bc83e77840a1584bdbfaf0c09e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"center\">\n", "<h1>[Dat...	mit
voytekresearch/nsaba	notebooks/demos/Brains.ipynb	1	1204338	null	mit
biosustain/cameo-notebooks	01-quick-start.ipynb	1	48844	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started with cameo " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "cameo reuses and extends model data structures defined by [cobrapy](https://opencobra.github.io/cobrapy/) (COnstrai...	apache-2.0
AdityoSanjaya/Billy-the-Kid	.ipynb_checkpoints/Skripsi Adinda-checkpoint.ipynb	1	130779	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Uploading data Adinda" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data <- read.csv(\"adinda.clean.csv\")" ] }, { "cel...	apache-2.0
pawni/sgld_online_approximation	DropoutMC_SGLD_LR.ipynb	1	8252	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiments using Dropout-MC\n", "\n", "We start by building the model and showing the basic inference procedure and calculation of the performance on the MNIST classification and the outlier detection task. Then perform mul...	mit
drdwitte/data4goodprojects	EVA/Data_Prep/FixMissingAddressData.ipynb	1	164130	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import pandas as pd\n", "import urllib.request\n", "import urllib.parse" ] }, { "cell_type": "code", "execution_count": 5, "m...	gpl-3.0
tuanavu/coursera-university-of-washington	machine_learning/3_classification/assigment/week2/module-3-linear-classifier-learning-assignment-blank.ipynb	1	28749	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing logistic regression from scratch\n", "\n", "The goal of this notebook is to implement your own logistic regression classifier. You will:\n", "\n", " * Extract features from Amazon product reviews.\n", ...	mit
gtesei/DeepExperiments	MNIST_for_beginners_noNN_noCONV_0.12.0-rc1.ipynb	1	184231	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST For ML Beginners\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A very simple MNIST classifier. See extensive documentation at http://tensorflow.org/tutorials/mnist/beginners/index.md" ] ...	apache-2.0
jrg365/gpytorch	examples/01_Exact_GPs/Spectral_Delta_GP_Regression.ipynb	1	328813	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spectral GP Learning with Deltas\n", "\n", "In this paper, we demonstrate another approach to spectral learning with GPs, learning a spectral density as a simple mixture of deltas. This has been explored, for example, as earl...	mit
balarsen/pymc_learning	Counting/Poisson and exponential.ipynb	1	435054	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Go from exponential to Poisson\n", "\n", "\n", "Also look to: Adams RP, Murray I, MacKay DJC. Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. Proceedings of the 26th Ann...	bsd-3-clause
phobson/seaborn	doc/introduction.ipynb	3	24808	{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ ".. _introduction:\n", "\n", ".. currentmodule:: seaborn\n", "\n", "An introduction to seaborn\n", "==========================\n", "\n", ".. raw:: html\n", "\n", " <div class=col-md-9>\n", "\n", ...	bsd-3-clause
ledeprogram/algorithms	class7/DecisionTrees-Validation.ipynb	1	20605	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs":...	gpl-3.0
Shinichi-Nakagawa/hatteberg	retrosheet_app/joey_votto.ipynb	1	111204	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/opt/pyenv/versions/py3.5.0_hatteberg/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is ...	mit
maubarsom/ORFan-proteins	orfan_2016_annotation/20161129_summarize_results/.ipynb_checkpoints/filter_blast_hits-checkpoint.ipynb	1	15724	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import re\n", "from glob import glob\n", "import requests\n", "from bs4 import BeautifulSoup\n", "import itertools" ] },...	mit
bt3gl/Machine-Learning-Resources	ml_notebooks/synthetic_features_and_outliers.ipynb	1	20176	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "synthetic_features_and_outliers.ipynb", "provenance": [], "collapsed_sections": [ "JndnmDMp66FL", "i5Ul3zf5QYvW", "jByCP8hDRZmM", "WvgxW0bUSC-c" ] }, "kernelspec": { "n...	gpl-2.0
msyriac/orphics	tutorials/Correlated maps.ipynb	1	388963	{ "cells": [ { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "from orphics import maps,cosmology,io,stats\n", "from enlib import enmap\n", "import numpy as np\n", "\n" ] }, { "cell_t...	bsd-2-clause
cberzan/kaggle-caterpillar	exploration/bagging.ipynb	2	11075	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pyla...	mit
Ykharo/notebooks	157 cosas de IPython que no sabías y nunca preguntaste (II).ipynb	2	40088	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "En la primera entrega vimos como usar la ayuda de IPython y como personalizar nuestras propias funciones m\u00e1gicas de ayuda.\n",...	bsd-2-clause
ishank26/nn_from_scratch	.ipynb_checkpoints/mlnn-checkpoint.ipynb	1	292757	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import sklearn as skl\n", "import sklearn.datas...	gpl-3.0
theandygross/TCGA_differential_expression	Notebooks/Preprocessing/unify_methylation_probes.ipynb	1	2016	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Pull common probes from all methylation arrays and create a new store. \n", "This is being done to allow for efficient selection of probes in a batch across tissues." ] }, { "cell_type": "code", "execution_count": 3, ...	mit
hobson/pug-ann	pug/ann/pybrain_weather_predictor.ipynb	1	8420	{ "metadata": { "name": "", "signature": "sha256:b12c61e3ea20c2b54fe453299c08ec05a96abdbc12c366c26b7d997b7c1bbc44" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from pug.ann.data import weather\n", ...	mit
ersh24/manoelgadi12	manoelgadi12/hello.ipynb	1	642	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def hello ():\n", " print (\"Hello World\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "p...	mit
M-R-Houghton/euroscipy_2015	cython/cy_tutorial.ipynb	1	264932	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EuroSciPy 2015, Stefan Behnel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See http://consulting.behnel.de/" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "co...	mit
vadim-ivlev/STUDY	handson-data-science-python/DataScience-Python3/.ipynb_checkpoints/MeanMedianMode-checkpoint.ipynb	1	23771	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mean, Median, Mode, and introducing NumPy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mean vs. Median" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's cr...	mit
GlobalFishingWatch/vessel-maps	utilities/pipa_paper/.ipynb_checkpoints/density_pings_byday-v2-checkpoint.ipynb	1	1094726	null	apache-2.0

WillenZh/deep-learning-project

tutorials/autoencoder/Convolutional_Autoencoder.ipynb

54

92975

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional Autoencoder\n", "\n", "Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data." ] }, { "cell_type": "code", "exe...

mit

google-research/language

language/multiberts/coref.ipynb

1

985078

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "O1t22v2ReiTx" }, "source": [ "# Application: Gender Bias in Coreference Systems\n", "\n", "This notebook walks through the analysis in Section 4 of [the paper](https://openreview.net/pdf?id=K0E_F0gFDgA). We'll look at accuracy a...

apache-2.0

gigjozsa/HI_analysis_course

chapter_00_preface/00_references_and_further_reading.ipynb

4

1722

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "***\n", "\n", "* [Content](../chapter_00_preface/00_00_introduction.ipynb#preface:sec:content)\n", "* [Glossary](../chapter_00_preface/00_01_glossary.ipynb#preface:sec:glossary)\n", "* [0. Preface](00_00_introduction.ip...

gpl-2.0

JanetMatsen/meta4

analysis/plot/150104_heat_maps_on_hyak.ipynb

2

13108

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import os\n", "import re\n", "imp...

bsd-3-clause

akloster/porekit-python

examples/squiggle_classifier_1/Read_Until_Efficiency.ipynb

1

129567

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Theoretical Efficiency of Read Until Enrichment\n", "\n", "The \"Read Until\" feature of the Oxford Nanopore sequencing technology means a program can see the data coming in at each pore and, dependend on that data, reject th...

isc

Ledoux/ShareYourSystem

Pythonlogy/draft/Directer/PreReadme.ipynb

1

9824

{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n\n\n#Directer\n\n##Doc\n----\n\n\n> \n> The Directer is a walker through the folders of the harddrive, \n> assuring a call of _DirectingCallbackFunction at each level.\n> \n> \n\n----\n\n<small>\nV...

mit

logmonster/odt_webapp

docs/chp4_eCommerce/fix_odt_jeymart_user_trx.ipynb

1

12010251

null

apache-2.0

cwharland/data-science-from-scratch

Working With Data.ipynb

2

146960

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from __future__ import division\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import math\n"...

mit

kurige/notebooks

notebooks/Test.ipynb

1

657631

{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": true, "input": [ "R = rand(500,500)\n", "R' * R" ], "language": "python", "metadata": {}, "outputs": [ ...

mit

david4096/bioapi-examples

python_notebooks/1kg_sequence_annotation_service.ipynb

1

12098

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## GA4GH 1000 Genomes Sequence Annotations Example\n", "\n", "This example illustrates how to access the sequence annotations for a given set of ...." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ...

apache-2.0

mwidner/WebArchiveTextTools

src/Text Mining with Web Archives.ipynb

2

4307

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cécile Alduy and I have been working with a team of undergraduate RAs and with some members of the Stanford University Libraries staff to ...

gpl-2.0

Joshuaalbert/IonoTomo

src/ionotomo/notebooks/UVWFrame.ipynb

1

16098

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import (absolute_import, unicode_literals, division,\n", " print_function)\n", "import numpy as np\n", "\n", "i...

apache-2.0

thushear/MLInAction

tensorflow/convolutional_tensor_net.ipynb

1

14550

{ "cells": [ { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# 倒库\n", "import tensorflow as tf\n", "import numpy as np\n", "from tensorflow.examples.tutorials.mnist import input_data" ] }, { "cell_type": "cod...

apache-2.0

NeuroDataDesign/seelviz

Tony/docs_ipynb_links/Testing+Structure+Tensor.ipynb

1

14818

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using tractography function in pipeline to generate structure tensors" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Import the main analysis module and tractography module. Use the analysis module...

apache-2.0

george1328/george1328.github.io

notebooks/regression/regression.ipynb

2

266913

{ "metadata": { "name": "", "signature": "sha256:b194c82f3b6d32a265a5b1170ecf38dd5d244f69b616c9cb46fd31ada4ebfbdc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Regression using SciKit Le...

apache-2.0

psychemedia/ou-robotics-vrep

robotVM/notebooks/Demo - Square 2 - Variables.ipynb

1

8721

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Traverse a Square - Part 2 - Variables\n", "\n", "In this notebook, we will introduce one of the most powerful ideas in programming: the *variable*.\n", "\n", "A variable is a container that we can reference *by name*...

apache-2.0

ES-DOC/esdoc-jupyterhub

notebooks/messy-consortium/cmip6/models/emac-2-53-aerchem/aerosol.ipynb

1

84330

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "**MIP Era**: CMIP6 \n", "**Institute**: MESSY-CONSORTIUM \n", "**Source ID**: EMAC-2-53-AERCHEM \n", ...

gpl-3.0

Kulbear/deep-learning-nano-foundation

DLND-tv-script-generation/dlnd_tv_script_generation.ipynb

1

46901

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# TV Script Generation\n", "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simps...

mit

Tahsin-Mayeesha/udacity-mlnd-deeplearning-capstone

Notebooks/Baseline.ipynb

1

9717

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_...

mit

locie/locie_notebook

base_python/itertools-fr.ipynb

1

676

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Itérer avec classe avec itertools !" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "l...

lgpl-3.0

JorisBolsens/PYNQ

Pynq-Z1/notebooks/examples/video_filters.ipynb

2

867931

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Software Grayscale and Sobel filters on HDMI input\n", "\n", "This example notebook will demonstrate two image filters using a snapshot from the HDMI input: <br>\n", "1. Fir...

bsd-3-clause

ES-DOC/esdoc-jupyterhub

notebooks/inm/cmip6/models/inm-cm4-8/seaice.ipynb

1

99801

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "**MIP Era**: CMIP6 \n", "**Institute**: INM \n", "**Source ID**: INM-CM4-8 \n", "**Topic...

gpl-3.0

sailuh/perceive

Parsers/SecLists/Reply-Parse.ipynb

1

51865

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Seclists reply parse\n", "\n", "__Example: http://seclists.org/fulldisclosure/2017/Jan/0__\n", "\n", "With each reply, we'll attempt to parse out the following:\n", "* raw reply text, without html tags\n", " ...

gpl-2.0

james-prior/cohpy

20161216-dojo-list-versus-square-brackets.ipynb

1

5613

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'hello'" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ ...

mit

rajathkumarmp/BinPy

BinPy/examples/notebook/ic/Series_7400/IC7430.ipynb

5

9446

{ "metadata": { "name": "", "signature": "sha256:7836c307211109fc0572a6007bfdcf683b081cce2759a0937677c98f07048209" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Usage of IC 7430" ] ...

bsd-3-clause

ocefpaf/secoora

notebooks/timeSeries/ssv/00-velocity_secoora.ipynb

2

10421

{ "metadata": { "kernelspec": { "codemirror_mode": { "name": "ipython", "version": 3 }, "display_name": "Iris (Python 2)", "language": "python", "name": "iris_python2" }, "name": "", "signature": "sha256:8958e88f73c0aaf001a82e539ac46040ab581cc5b06d4bc954f2deb08c593345" }, "nbformat": 3...

mit

loganjt/DropletJumpWedge

Repos_Data/ExitTime_Optimization.ipynb

2

6559

{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, glob, numpy as np, csv, math\n", "import numpy.ma as ma\n", "import matplotlib.pyplot as plt\n", "import matplotlib.colors as colors\n", "impo...

gpl-3.0

GoogleCloudPlatform/cloudml-samples

notebooks/tensorflow/census/estimator/trainer/task.ipynb

1

9100

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Copyright 2016 Google LLC \n", " \n", " Licensed under the Apache License, Version 2.0 (the \"License\"); \n", " you may not use this file except in compliance with the License. \n", " You may obtain a copy of the L...

apache-2.0

xR86/ml-stuff

kaggle/enron-email/Initial.ipynb

1

7305933

null

mit

poppy-project/community-notebooks

tutorials-education/poppy-humanoid_poppy-torso__vrep_installation et prise en main/poppy réel/Construction/construction & installation (poppy réel).ipynb

2

9747

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"png/poppy.png\" HEIGHT=200 WIDTH=200 ALIGN=right>\n", "<img src=\"png/inria.jpg\" HEIGHT=150 WIDTH=325 ALIGN=left >" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"png/diagram...

lgpl-3.0

dsiufl/2015-Fall-Hadoop

instructor-notes/3-pyspark-wordcount.ipynb

2

5478

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spark version of wordcount examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prepare the pyspark environment." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { ...

mit

chbehrens/pr_bc_connectivity-1

CBCX_ON_CBC_contact_comparison.ipynb

2

42504

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparision between the size of basal contacts of CBCX and OFF-CBCs" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as ...

gpl-3.0

nkmk/python-snippets

notebook/argument_expand_dict.ipynb

1

4427

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "def func(arg1, arg2, arg3):\n", " print('arg1 =', arg1)\n", " print('arg2 =', arg2)\n", " print('arg3 =', arg3)" ] }, { "cell_type": "code", "execution_count": 2,...

mit

agile-geoscience/welly

docs/_userguide/Projects.ipynb

1

99093

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Projects\n", "\n", "Wells are one of the fundamental objects in welly.\n", "\n", "Well objects include collections of Curve objects. Multiple Well objects can be stored in a Project.\n", "\n", "On this page, w...

apache-2.0

huilyu2/DataVisualization

project-spring2017/part2/Part2-ilnkage-version 2.ipynb

1

87421

{ "cells": [ { "cell_type": "code", "execution_count": 212, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import plotly \n", "plotly.tools.set_credentials_file(username='yuxiangling0809', api_key='vJxzgz9EZWkJZdHur9A8')" ] }, { "cell_type": "code", "executi...

mit

mne-tools/mne-tools.github.io

dev/_downloads/00e78bba5d10188fcf003ef05e32a6f7/decoding_time_generalization_conditions.ipynb

1

5309

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Decoding s...

bsd-3-clause

bryantbiggs/movie_torrents

notebooks/numbers_scrape_validate.ipynb

2

54312

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "sys.path.append('../src')\n", "\n", "from numbers_scraper import NumbersScraper\n", "SCRAPER = NumbersScraper()" ] }, { "cell_ty...

mit

h-mayorquin/time_series_basic

presentations/2016-01-21(Wall-Street-Letter-Latency-Prediction).ipynb

1

126378

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Prediction of text with Nexa Letter Latency.\n", "This notbook is for seeing how much the delay between the code vector and the code is related to the accuaracy of the prediciton." ] }, { "cell_type": "code", "execut...

bsd-3-clause

tschinz/iPython_Workspace

02_WP/Wavedrom/example.ipynb

1

2431

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "slideshow": { "slide_type": "-" } }, "outputs": [], "source": [ "import wavedrom\n", "\n", "a = {'signal': [\n", " {'name': 'clk', 'wave': 'p.....|...'},\n", " {'n...

gpl-2.0

GoogleCloudPlatform/training-data-analyst

courses/machine_learning/feateng/feateng.ipynb

1

25179

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1> Feature Engineering </h1>\n", "\n", "In this notebook, you will learn how to incorporate feature engineering into your pipeline.\n", "<ul>\n", "<li> Working with feature columns </li>\n", "<li> Adding feature c...

apache-2.0

CalPolyPat/phys202-2015-work

days/day07/Interact.ipynb

1

15480

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Interact" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `interact` function (`IPython.html.widgets.interact`) automatically creates a graphical user interface (GUI) for exploring code and ...

mit

crdietrich/sparklines

Pandas Sparklines Demo.ipynb

1

305328

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Sparklines in Pandas\n", "\n", "Sparklines are small unlabeled plots, used to visually convey an idea in a small space. This script creates sparklines in a Pandas DataFrame which can then be displayed inline in a Jupyter No...

mit

MPIBGC-TEE/CompartmentalSystems

notebooks/nonl_gcm_3p_many_params/means_and_sd_for_v0_and_v5.ipynb

1

8307545

null

mit

SiggyF/notebooks

soapexample.ipynb

1

41883

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Using domeintabellen webservice" ] }, { "cell_type": "code", "collapsed": false, "input"...

gpl-3.0

mit-eicu/eicu-code

notebooks/medication.ipynb

1

87315

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# medication\n", "\n", "The medications table reflects the active medication orders for patients. These are orders but do not necessarily reflect administration to the patient. For example, while existence of data in the infusi...

mit

noppanit/machine-learning

animated-graph/Animated Graphs.ipynb

1

50162

{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from moviepy.video.io.bindings import mplfig_to_npimage\n", ...

mit

tpin3694/tpin3694.github.io

scala/find_largest_key_or_value_in_a_map.ipynb

1

2495

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Title: Find Largest Key Or Value In A Map \n", "Slug: find_largest_key_or_value_in_a_map \n", "Summary: Find Largest Key Or Value In A Map Using Scala. \n", "Date: 20...

mit

opengeostat/pygslib

pygslib/Ipython_templates/backtr_raw.ipynb

1

76221

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Testing the back normalscore transformation\n", "========\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "application/javascript": [ "\n", ...

mit

Leguark/GeMpy

legacy/Geothealler big function.ipynb

2

250862

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/bl3/anaconda3/lib/python3.5/site-packages/ipykernel/__main__.py:18: DeprecationWarning: stack(*tensors) ...

mit

henrysky/astroNN

demo_tutorial/VAE/variational_autoencoder_demo.ipynb

1

951028

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Variational Autoencoder demo with 1D data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is [astroNN](https://github.com/henrysky/astroNN), please take a look if you are interested in astronomy...

mit

turbomanage/training-data-analyst

blogs/goes16/maria/hurricanes2017.ipynb

2

213981

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 2017 Hurricane Tracks\n", "\n", "Demonstrates how to plot all the North American hurricane tracks in 2017, starting from the BigQuery public dataset." ] }, { "cell_type": "code", "execution_count": null, "met...

apache-2.0

thatguyandy27/python-sandbox

l3/Lecture8 - Using Arrays and Scalars.ipynb

1

3793

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { ...

mit

Ledoux/ShareYourSystem

Pythonlogy/ShareYourSystem/Readme.ipynb

1

1990

{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": [ "#ShareYourSystem\n", "\n" ], "cell_type": "markdown", "metadata": {} }, { "source": "\n\n\n##More Descriptions at...

mit

jorgehatccrma/pyGrFNN

notebooks/utils/Oscillator Regimes.ipynb

2

25139

{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib2tikz import save as tikz_save" ] }, ...

bsd-3-clause

adityaka/misc_scripts

python-scripts/data_analytics_learn/link_pandas/Ex_Files_Pandas_Data/Exercise Files/05_05/Final/.ipynb_checkpoints/Annotations-checkpoint.ipynb

1

4054

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Plot Annotations</h1>" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "impor...

bsd-3-clause

probml/pyprobml

notebooks/misc/linreg_divorce_numpyro.ipynb

1

93188

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "linreg_divorce_numpyro.ipynb", "provenance": [], "toc_visible": true, "authorship_tag": "ABX9TyMAtUs/BjAM8bCfZRQ7MotY", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, ...

mit

saketkc/notebooks

python/coursera-BayesianML/04_mcmc_assignment.ipynb

1

355126

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mime...

bsd-2-clause

irockafe/revo_healthcare

notebooks/MTBLS17/exploratory/MTBLS17_uhplc_pos_classifer_no_retcor.ipynb

1

266082

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h2> After performing centwav at 15ppm, then grouping with bw=2. No retention correction applied </h2>\n", "Remember, this dataset is weird. It has replicates of all measurements. Make sure to split them before doing and ML stuff. ...

mit

karlstroetmann/Formal-Languages

Ply/Conflicts-Resolved.ipynb

1

8073

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "with open (\"../style.css\", \"r\") as file:\n", " css = file.read()\n", "HTML(css)" ] }, { "cell_type": "markdown", "meta...

gpl-2.0

reallyasi9/riddlers

lower-face-dice-game/lower_face_dice_game.ipynb

1

211567

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "def roll_me(N=100):\n", " rolls = 1 # Always have to roll at least once\n", " r1 = np.random.randint(1, N+1) # Note that randint...

gpl-3.0

wasit7/cs634

2017/week07/DQN.ipynb

1

22009

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "[2017-10-14 20:47:15,571] Making new env: FrozenLake-v0\n" ] } ], "source": [ "%matplotlib inline\n", "import matp...

bsd-2-clause

whitews/flow_rate_qc

flow_rate_ks.ipynb

1

4071165

null

bsd-3-clause

jagarzone6/cmos

notebooks/Interface using tkinter.ipynb

2

3250

{ "cells": [ { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from tkinter import *\n", "from tkinter import ttk\n", "import math\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as p...

mit

jbwhit/WSP-312-Tips-and-Tricks

notebooks/02-Visualization-and-code-organization.ipynb

1

4397

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import absolute_import, division, print_function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Notebo...

mit

davebshow/prelims

modularity-no_auth_patron.ipynb

1

42682

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import networkx as nx\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "plt.rcPara...

mit

spatchcock/monetary_economics_python

notebooks/.ipynb_checkpoints/scotland-checkpoint.ipynb

1

181895

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "$$\n", "\\begin{array}{lcl}\n", "Y = C + G \\hspace{1cm} & (1) \\\\\n", "T = \\theta Y \\hspace{1cm} & (2) \\\\\n"...

mit

marburg-open-courseware/gmoc

docs/mpg-if_error_continue/notebooks/ggplot.ipynb

1

304517

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "from ggplot import *\n", "\n", "import pandas as pd\n", "import numpy as np\n", "\n", "?ggplot" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}...

mit

mliu49/RMG-stuff

Kinetics/TestFamilies.ipynb

1

4535

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import rmgpy\n", "from rmgpy.data.rmg import RMGDatabase\n", "from rmgpy.rmg.react import *\n", "from rmgpy.reaction import Reaction\n", "from rmgpy.m...

mit

caganze/wisps

notebooks/.ipynb_checkpoints/y standards-checkpoint.ipynb

1

90770

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/users/caganze/research/splat\n", "\n", "\n", "Welcome to the Spex Prism Library Analysis Toolkit (SPLAT)!\n", "I...

mit

willettk/insight

notebooks/neural_networks_and_deep_learning.ipynb

1

26612

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Work with http://neuralnetworksanddeeplearning.com/" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "from ma...

apache-2.0

nagordon/mechpy

tutorials/Composite_Plate_Mechanics_with_Python_Theory.ipynb

1

47227

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "- - - -\n", "# Mechpy Tutorials\n", "a mechanical engineering toolbox\n", "\n", "source code - https://github.com/nagordon/mechpy \n", "documentation - https://nagordon.github.io/mechpy/web/ \n", "\n", "- ...

mit

lyftzeigen/MachineLearningLessons

KerasFFNWeatherPrediction/KerasFFNWeatherPrediction.ipynb

1

154741

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Keras FFN weather prediction in Rostov-on-Don" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using ...

mit

paulovn/ml-vm-notebook

vmfiles/IPNB/Examples/d Scala/01 Hello world.ipynb

1

2989

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hello World in a Scala Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is a first try with a Scala (Spark) kernel. Please be patient on execution of the first cell, it takes a ...

bsd-3-clause

brandon-rhodes/pycon-pandas-tutorial

All.ipynb

1

318960

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "idx = pd.IndexSlice" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collaps...

mit

pligor/predicting-future-product-prices

04_time_series_prediction/24_price_history_seq2seq-full_dataset_testing.ipynb

2

100225

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -*- coding: UTF-8 -*-\n", "#%load_ext autoreload\n", "%reload_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count":...

agpl-3.0

versae/DH2304

class06.ipynb

1

269729

{ "metadata": { "name": "", "signature": "sha256:67d77d14b77e2ab47288c369cce432a58ccc63bc83e77840a1584bdbfaf0c09e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"center\">\n", "<h1>[Dat...

mit

voytekresearch/nsaba

notebooks/demos/Brains.ipynb

1

1204338

null

mit

biosustain/cameo-notebooks

01-quick-start.ipynb

1

48844

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started with cameo " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**cameo** reuses and extends model data structures defined by [cobrapy](https://opencobra.github.io/cobrapy/) (**CO**nstrai...

apache-2.0

AdityoSanjaya/Billy-the-Kid

.ipynb_checkpoints/Skripsi Adinda-checkpoint.ipynb

1

130779

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Uploading data Adinda" ] }, { "cell_type": "code", "execution_count": 96, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data <- read.csv(\"adinda.clean.csv\")" ] }, { "cel...

apache-2.0

pawni/sgld_online_approximation

DropoutMC_SGLD_LR.ipynb

1

8252

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Experiments using Dropout-MC\n", "\n", "We start by building the model and showing the basic inference procedure and calculation of the performance on the MNIST classification and the outlier detection task. Then perform mul...

mit

drdwitte/data4goodprojects

EVA/Data_Prep/FixMissingAddressData.ipynb

1

164130

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import pandas as pd\n", "import urllib.request\n", "import urllib.parse" ] }, { "cell_type": "code", "execution_count": 5, "m...

gpl-3.0

tuanavu/coursera-university-of-washington

machine_learning/3_classification/assigment/week2/module-3-linear-classifier-learning-assignment-blank.ipynb

1

28749

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Implementing logistic regression from scratch\n", "\n", "The goal of this notebook is to implement your own logistic regression classifier. You will:\n", "\n", " * Extract features from Amazon product reviews.\n", ...

mit

gtesei/DeepExperiments

MNIST_for_beginners_noNN_noCONV_0.12.0-rc1.ipynb

1

184231

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST For ML Beginners\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A very simple MNIST classifier. See extensive documentation at http://tensorflow.org/tutorials/mnist/beginners/index.md" ] ...

apache-2.0

jrg365/gpytorch

examples/01_Exact_GPs/Spectral_Delta_GP_Regression.ipynb

1

328813

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Spectral GP Learning with Deltas\n", "\n", "In this paper, we demonstrate another approach to spectral learning with GPs, learning a spectral density as a simple mixture of deltas. This has been explored, for example, as earl...

mit

balarsen/pymc_learning

Counting/Poisson and exponential.ipynb

1

435054

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Go from exponential to Poisson\n", "\n", "\n", "Also look to: Adams RP, Murray I, MacKay DJC. Tractable nonparametric Bayesian inference in Poisson processes with Gaussian process intensities. Proceedings of the 26th Ann...

bsd-3-clause

phobson/seaborn

doc/introduction.ipynb

3

24808

{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ ".. _introduction:\n", "\n", ".. currentmodule:: seaborn\n", "\n", "An introduction to seaborn\n", "==========================\n", "\n", ".. raw:: html\n", "\n", " <div class=col-md-9>\n", "\n", ...

bsd-3-clause

ledeprogram/algorithms

class7/DecisionTrees-Validation.ipynb

1

20605

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs":...

gpl-3.0

Shinichi-Nakagawa/hatteberg

retrosheet_app/joey_votto.ipynb

1

111204

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/opt/pyenv/versions/py3.5.0_hatteberg/lib/python3.5/site-packages/matplotlib/__init__.py:872: UserWarning: axes.color_cycle is ...

mit

maubarsom/ORFan-proteins

orfan_2016_annotation/20161129_summarize_results/.ipynb_checkpoints/filter_blast_hits-checkpoint.ipynb

1

15724

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import re\n", "from glob import glob\n", "import requests\n", "from bs4 import BeautifulSoup\n", "import itertools" ] },...

mit

bt3gl/Machine-Learning-Resources

ml_notebooks/synthetic_features_and_outliers.ipynb

1

20176

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "synthetic_features_and_outliers.ipynb", "provenance": [], "collapsed_sections": [ "JndnmDMp66FL", "i5Ul3zf5QYvW", "jByCP8hDRZmM", "WvgxW0bUSC-c" ] }, "kernelspec": { "n...

gpl-2.0

msyriac/orphics

tutorials/Correlated maps.ipynb

1

388963

{ "cells": [ { "cell_type": "code", "execution_count": 84, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "from orphics import maps,cosmology,io,stats\n", "from enlib import enmap\n", "import numpy as np\n", "\n" ] }, { "cell_t...

bsd-2-clause

cberzan/kaggle-caterpillar

exploration/bagging.ipynb

2

11075

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pyla...

mit

Ykharo/notebooks

157 cosas de IPython que no sabías y nunca preguntaste (II).ipynb

2

40088

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "En la primera entrega vimos como usar la ayuda de IPython y como personalizar nuestras propias funciones m\u00e1gicas de ayuda.\n",...

bsd-2-clause

ishank26/nn_from_scratch

.ipynb_checkpoints/mlnn-checkpoint.ipynb

1

292757

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "scrolled": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import sklearn as skl\n", "import sklearn.datas...

gpl-3.0

theandygross/TCGA_differential_expression

Notebooks/Preprocessing/unify_methylation_probes.ipynb

1

2016

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Pull common probes from all methylation arrays and create a new store. \n", "This is being done to allow for efficient selection of probes in a batch across tissues." ] }, { "cell_type": "code", "execution_count": 3, ...

mit

hobson/pug-ann

pug/ann/pybrain_weather_predictor.ipynb

1

8420

{ "metadata": { "name": "", "signature": "sha256:b12c61e3ea20c2b54fe453299c08ec05a96abdbc12c366c26b7d997b7c1bbc44" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from pug.ann.data import weather\n", ...

mit

ersh24/manoelgadi12

manoelgadi12/hello.ipynb

1

642

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def hello ():\n", " print (\"Hello World\")" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "p...

mit

M-R-Houghton/euroscipy_2015

cython/cy_tutorial.ipynb

1

264932

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EuroSciPy 2015, Stefan Behnel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "See http://consulting.behnel.de/" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "co...

mit

vadim-ivlev/STUDY

handson-data-science-python/DataScience-Python3/.ipynb_checkpoints/MeanMedianMode-checkpoint.ipynb

1

23771

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mean, Median, Mode, and introducing NumPy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mean vs. Median" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's cr...

mit

GlobalFishingWatch/vessel-maps

utilities/pipa_paper/.ipynb_checkpoints/density_pings_byday-v2-checkpoint.ipynb

1

1094726

null

apache-2.0