Datasets:
codeparrot
/
github-jupyter

Dataset card Data Studio Files
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bdearlove/pangea-round2
sequences/regional.ipynb
2
4544
{ "metadata": { "name": "", "signature": "sha256:d8ccc5ad0ee84e171c7fd2be89e045545192b38bf0b1d3a650db34659ed9a91e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Regional data\n", "\n", "Need ...
mit
wy36101299/ipynb-file
collaborative-filtering.ipynb
1
15618
{ "metadata": { "name": "", "signature": "sha256:5fafcfb5bc8b6fda388fba2277bafd18f29cd9777c59c190754455eb838b8996" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "# linalg:Line...
mit
NeuroDataDesign/seelviz
jon/algorithms/connectivity2.ipynb
2
6301580
null
apache-2.0
ituethoslab/navcom-2017
exercises/Week 7-Situational Mapping/Week 7-Situational Mapping.ipynb
1
3736
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Situational mapping\n", "\n", "Exercise: Mapping actors, relations and discourse (worlds of meaning, including technology)\n", "\n", "Today’s exercise will explore the mapping of actors, their associations, and the so...
gpl-3.0
MridulS/BinPy
BinPy/examples/notebook/Gates/NOR.ipynb
1
3926
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bsd-3-clause
dereneaton/ipyrad
newdocs/API-analysis/cookbook-locus_builder.ipynb
1
2523
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h2><span style=\"color:gray\">ipyrad-analysis toolkit:</span> locus_builder</h2>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h5><span style=\"color:red\">(Reference only method)</span></h5>\n", ...
gpl-3.0
rbiswas4/AnalyzeSN
examples/Demo_usingResChar.ipynb
1
4062
{ "cells": [ { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import analyzeSN" ] }, { "cell_type": "code",...
mit
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies
ex00-Introduction Life is short, use Python.ipynb
1
10839
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", "\n", ".rendered_html {\n", " font-family: \"proxima-nova\", helvetica;\n", " font-size: 130%;\n", " line-height...
mit
wy1iu/sphereface
tools/caffe-sphereface/examples/02-fine-tuning.ipynb
13
480512
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fine-tuning a Pretrained Network for Style Recognition\n", "\n", "In this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the par...
mit
dereneaton/ipyrad
testdocs/analysis/cookbook-mb-empirical-calibration.ipynb
1
3710003
null
gpl-3.0
ThierryMondeel/FBA_python_tutorial
FBA_tutorials/minibook-2nd-code-master/chapter4/42-mpl.ipynb
1
5253
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": "## matplotlib and seaborn essentials" }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": "import numpy as np\nimport matplotlib.pyplot as plt\nimport sea...
mit
chbrandt/zyxw
docs/python_notebooks/.ipynb_checkpoints/VO_CatalogServices_search-checkpoint.ipynb
1
106496
{ "metadata": { "name": "", "signature": "sha256:cbf4d23ef1ebe0a367156885dad90271ca5109563a038672023422084ecd25e7" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Searching for X-Ray catalo...
gpl-2.0
XinyiGong/pymks
notebooks/filter.ipynb
2
42342
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Filter Example\n", "\n", "This example demonstrates the connection between MKS and signal\n", "processing for a 1D filter. It shows that the filter is in fact the\n", "same as the influence coefficients and, thus, app...
mit
turbomanage/training-data-analyst
courses/machine_learning/deepdive2/structured/labs/3c_bqml_dnn_babyweight.ipynb
1
13294
{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "3o8Qof7Cy165" }, "source": [ "# LAB 3c: BigQuery ML Model Deep Neural Network.\n", "\n", "**Learning Objectives**\n", "\n", "1. Create and evaluate DNN model with BigQuery ML\n", "1. Create...
apache-2.0
kubeflow/kfp-tekton-backend
samples/core/ai_platform/ai_platform.ipynb
1
11162
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chicago Crime Prediction Pipeline\n", "\n", "An example notebook that demonstrates how to:\n", "* Download data from BigQuery\n", "* Create a Kubeflow pipeline\n", "* Include Google Cloud AI Platform components to...
apache-2.0
JasperHG90/reclaimnaija-data
Elections_2015/python/.ipynb_checkpoints/reclaimnaija2015-checkpoint.ipynb
1
181
{ "metadata": { "name": "", "signature": "sha256:adb0c9b12deef982aba1d49862ea9e496203bd1ae44bd60018b5986411dddb9d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [] }
mit
vandy-astro-hacks/test-repository
yt_intro.ipynb
1
46589
{ "metadata": { "name": "yt_intro" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "What is yt?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...
gpl-2.0
phoebe-project/phoebe2-docs
development/tutorials/beaming_boosting.ipynb
2
77857
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Beaming and Boosting\n", "============================\n", "\n", "Due to concerns about accuracy, support for Beaming & Boosting has been disabled as of the 2.2 release of PHOEBE (although we hope to bring it back in a futu...
gpl-3.0
autumn-lake/Facebook-V-Predicting-Check-Ins
mahalanobis-Copy2.ipynb
2
10028
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n", "import matplotlib.pyplot as plt\n", "from scipy.stats import gaussian_kde\n", "import...
mit
mitliagkas/graphs
Analysis.ipynb
1
43285
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg' \n", "import numpy as np\n", "import scipy as sp...
mit
srippa/nn_deep
NN playground.ipynb
1
17284
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "## resourcus\n", "* [I am trask blog - simple introduction to NN](http://iamtrask.github.io/)\n", "* [Neural bnetwork tutorial](http://www.existor.com/en/news-neural-networks.html) - walk all the way. [A similar tutor...
mit
huizhuzhao/jupyter_notebook
tests/nn_manifold_topology.ipynb
1
82991
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### 构造神经网络模型对两条曲线数据点进行二分类，详细信息参考博客\n", "https://huizhuzhao.github.io/2017/01/16/neural-networks-manifolds-topology.html" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, ...
mit
statkraft/shyft-doc
notebooks/repository/repositories-intro.ipynb
1
581066
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exposing the API\n", "\n", "## Introduction\n", "At its core, Shyft provides functionality through an API (Application Programming Interface). All the functionality of Shyft is available through this API.\n", "\n", ...
lgpl-3.0
ahirner/TabulaRazr-OS
.ipynb_checkpoints/TableParser4-checkpoint.ipynb
1
11827
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#TabulaRazr - specific to calculate - TABLE Parser\n", "#Infers a table with arbitrary number of columns from reoccuring patterns in text lines\n", "#(c) Alexand...
agpl-3.0
theJollySin/data_science_from_scratch
chapters/21_network_analysis/network_analysis.ipynb
1
13131
{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "{'friends': [{'friends': [{...}, {'friends': [{...}, {...}, {'friends': [{...}, {...}, {'friends': [{...}, {'f...
mit
jforbess/pvlib-python
docs/tutorials/tmy_to_power.ipynb
1
1559771
null
bsd-3-clause
IST256/learn-python
content/lessons/04-Iterations/LAB-Iterations.ipynb
1
15740
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Class Coding Lab: Iterations\n", "\n", "The goals of this lab are to help you to understand:\n", "\n", "- How loops work.\n", "- The difference between definite and indefinite loops, and when to use each.\n", ...
mit
supesolutions/portia-iot
examples/PHP.ipynb
1
19925
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Querying portia - Data fetching with PHP" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making HTTP requests using Python - Checking credentials" ] }, { "cell_type": "markdown", "met...
mit
openworm/ChannelWorm
tests/scidash/EGL-19_IV.ipynb
3
48538
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Validating a channel model IV curve with data from an experiment" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import rickpy\n", ...
mit
minesh1291/Practicing-Kaggle
zillow2017/H2Opy_v0.ipynb
1
226257
{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#import-Packages\" data-toc-modified-id=\"import-Packages-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>import Packages</a></div><div ...
gpl-3.0
tensorflow/docs-l10n
site/zh-cn/guide/keras/custom_callback.ipynb
1
22377
{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "b518b04cbfe0" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...
apache-2.0
chichilalescu/bfps
meta/Velocity gradient.ipynb
1
5584
{ "cells": [ { "cell_type": "code", "execution_count": 31, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-Axx**2/2 - Axy*Ayx - Axz*Azx - Ayy**2/2 - Ayz*Azy - Azz**2/2\n", "-Axx*(Axx**2/3 + Axy*Ayx + Axz*Azx) ...
gpl-3.0
TomTranter/OpenPNM
examples/tutorials/Intro to OpenPNM - Advanced.ipynb
1
885497
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Tutorial 3 of 3: Advanced Topics and Usage\n", "\n", "**Learning Outcomes**\n", "\n", "* Use different methods to add boundary pores to a network\n", "* Manipulate network topology by adding and removing pores and...
mit
RyanAlberts/Springbaord-Capstone-Project
Statistics_Exercises/sliderule_dsi_inferential_statistics_exercise_2.ipynb
1
3820
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examining Racial Discrimination in the US Job Market\n", "\n", "### Background\n", "Racial discrimination continues to be pervasive in cultures throughout the world. Researchers examined the level of racial discrimination...
mit
mne-tools/mne-tools.github.io
dev/_downloads/64b41c961a5966a9f21a532d9667c8a0/xhemi.ipynb
1
2644
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Cross-hemi...
bsd-3-clause
quantopian/research_public
notebooks/lectures/Case_Study_Comparing_ETFs/answers/notebook.ipynb
2
78420
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Exercises: Comparing ETFs - Answer Key\n", "By Christopher van Hoecke, Maxwell Margenot, and Delaney Mackenzie\n", "\n", "\n", "## Lecture Link :\n", "https://www.quantopian.com/lectures/...
apache-2.0
Danghor/Algorithms
Python/Chapter-09/Dijkstra.ipynb
2
20368
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "with open('../style.css') as file:\n", " css = file.read()\n", "HTML(css)" ] }, { "cell_type": "markdown", "metadata": {},...
gpl-2.0
arcyfelix/Courses
18-03-07-Deep Learning With Python by François Chollet/Chapter 5.3 - Using a pre-trained convnet.ipynb
2
290199
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 5.3 - Using a pre-trained convnet" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Tenso...
apache-2.0
Miceuz/rs485-moist-sensor
doc/measurements/Sensor value in free air.ipynb
1
136781
{ "metadata": { "name": "", "signature": "sha256:d20bd157a887f270c5b33c2d83f9f51cf94b23021682c006223bf2428cf5e0c0" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "d = loadtxt('./initial-value-distribution-c...
apache-2.0
drabastomek/learningPySpark
Chapter06/LearningPySpark_Chapter06.ipynb
1
37409
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introducing ML package of PySpark" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Predict chances of infant survival with ML" ] }, { "cell_type": "markdown", "metadata": {}, "sourc...
gpl-3.0
yashdeeph709/Algorithms
PythonBootCamp/Complete-Python-Bootcamp-master/.ipynb_checkpoints/Chained Comparison Operators-checkpoint.ipynb
2
4411
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chained Comparison Operators\n", "\n", "An interesting feature of Python is the ability to *chain* multiple comparisons to perform a more complex test. You can use these chained comparisons as a shorthand for larger Boolean E...
apache-2.0
jasonding1354/ScalaFAQ
collections/collection_hierarchy.ipynb
1
26137
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 集合库继承层次\n", "- 集合继承层次从TraversableOnce特质开始。这个特质代表至少能遍历一次的集合。这个特质对Traversable和Iterator进行了抽象。\n", "- Iterator代表了一个数据流，前进到下一个数据项意味着“消费”了当前数据项（也就是只能遍历一次）。\n", "- Traversable代表提供了遍历全部数据的机制的集合，而且能够反复地遍历。\n", "- 最后继承层次分裂为三个分...
mit
gogrean/SurfFit
examples/notebooks/SB across core in MACS J0717.ipynb
2
399451
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import os\n", "import pickle\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import matplotlib.tic...
gpl-3.0
chengsoonong/mclass-sky
mclearn/knfst/python/test.ipynb
3
74253
{ "cells": [ { "cell_type": "code", "execution_count": 347, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import scipy as sp\n", "import pandas as pd\n", "import urllib.request\n", "import os\n", "import shutil\n", "import ta...
bsd-3-clause
pragyasresta29/Restaurant-Recommendation-System
recommendation system/getRecommendations.ipynb
1
9417
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pickle" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def load_...
gpl-3.0
martindavid/code-sandbox
data-playground/Inspiration_Exploration_2.ipynb
1
258356
{ "cells": [ { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sqlalchemy import create_engine\n", "engine = create_engine('postgresql://localhost:5432/ci_inspirations')" ] }, { "cell_type": "code", "executi...
mit
kimkipyo/dss_git_kkp
통계, 머신러닝 복습/160620월_17일차_나이브 베이즈 Naive Bayes/2.실전 예제.ipynb
1
30021
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# 베르누이의 경우 실습 예제" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "t...
mit
arvind-iyer/socdata
Explainer notebook.ipynb
2
29253
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Explainer Notebook" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a notebook produced in the course 02806 - Social data analysis and visualization at DTU in Denmark, Spring 2017 [2]. The int...
bsd-3-clause
adamsteer/nci-notebooks
.ipynb_checkpoints/Point cloud to HDF-checkpoint.ipynb
1
1036690
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## What is the proposed task:\n", "- ingest some liDAR points into a HDF file\n", "- ingest the aircraft trajectory into the file\n", "- anything else\n", "\n", "...and then **extract** data from the HDF file at dif...
apache-2.0
yihaochen/FLASHtools
synchrotron/Synchrotron_Polarization_OffAxis_Difference_102.ipynb
1
7559
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import yt\n", "import logging\n", "logging.getLogger('yt').setLevel(logging.ERROR)\n", ...
gpl-2.0
nerdcommander/scientific_computing_2017
lesson2/Lesson2_team.ipynb
1
12892
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Unit 1: Programming Basics" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Lesson 2: Writing Useful Functions" ] }, { "cell_type": "markdown", "metadata": {}...
mit
chichilalescu/python-for-scientific-computing
TA Class Notes/Day1.ipynb
1
2463
{ "metadata": { "name": "", "signature": "sha256:3b3e900f8d18695ad9ca648e296ca5137abab0392f31b2efd5bf40f137cf99e6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "print 'Hello World'\n", "print type('...
gpl-3.0
trsherborne/learn-python
lesson4.ipynb
1
15933
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## LSESU Applicable Maths Python Lesson 4\n", "###### 15/11/16" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Today we will be learning about\n", "* Data Structures - Official documentation on...
mit
wtchg-kwiatkowski/pfx-paper-2015
supplementary/notebooks/hotspots_discovery.ipynb
1
23860
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "docker image cggh/biipy:v1.6.0\n" ] }, { "data": { "text/html": [ "<style type=...
mit
anshbansal/anshbansal.github.io
udacity_data_science_notes/intro_machine_learning/lesson_15/lesson_15.ipynb
1
609
{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## Tying it all together\n", "\n", "![](steps.png)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { ...
mit
hktxt/MachineLearning
PyTorch Tutorials/HJ.ipynb
1
326722
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function, division\n", "\n", "import torch\n", "import torch.nn as nn\n", "import torch.optim as optim\n", "from torch.au...
gpl-3.0
r-shekhar/NYC-transport
15_dataframe_analysis/spatialjoin_geopandas_dask.ipynb
1
12538675
null
bsd-3-clause
ramhiser/Keras-Tutorials
notebooks/06_autoencoder.ipynb
1
246652
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autoencoders\n", "\n", "I've been exploring how useful [autoencoders](https://en.wikipedia.org/wiki/Autoencoder) are and how painfully simple they are to implement in [Keras](https://keras.io/). In this post, my goal is to be...
mit
james-prior/cohpy
20170907-dojo-decimal.ipynb
1
4411
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "from decimal import Decimal, getcontext, ROUND_HALF_EVEN" ] }, { "cell_type": "code", "execution_count": 2, "...
mit
Jackporter415/phys202-project
Base_Question.ipynb
2
20499
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ ":0: FutureWarning: IPython widgets are experimental and may change in the future.\n" ] } ], "so...
mit
JKeun/project-02-watcha
03_model/05_Standard_Score(random_pick).ipynb
1
2979
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### 랜덤으로 pred할 때의 기준 스코어" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [], "source": [ "x1 = np.random.randint(1,6,1000)\n", "x2 = np.random.rand...
mit
the-deep-learners/study-group
neural-networks-and-deep-learning/src/run_network.ipynb
3
7242
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Network from Nielsen's Chapter 1\n", "http://neuralnetworksanddeeplearning.com/chap1.html#implementing_our_network_to_classify_digits" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "sou...
mit
anne-urai/pupilUncertainty
newstuff/confidence_updating_4Armin.ipynb
2
471928
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "These analyses and simulations use data from Urai et al. 2017, and aim to further pinpoint the relationship between several proxies for confidence on the previous trial.\n", "\n", "If you use this work in any way, or see someth...
mit
statsmodels/statsmodels.github.io
v0.13.1/examples/notebooks/generated/pca_fertility_factors.ipynb
2
315749
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# statsmodels Principal Component Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "*Key ideas:* Principal component analysis, world bank data, fertility\n", "\n", "In this notebook, we ...
bsd-3-clause
mdeff/ntds_2017
projects/reports/face_manifold/NTDS_Project.ipynb
1
542102
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Manifold Learning on Face Data\n", "\n", "**Atul Kumar Sinha, Karttikeya Mangalam and Prakhar Srivastava**\n", "\n", "In this project, we explore manifold learning on face data to embed high dimensional face images in...
mit
ccwang002/2014-ggplot2-intro
Rcode/play_earthquake_map/raw_earthquake.ipynb
4
3058
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import re" ], "language": "python", "metadata": {}, "outputs": [], "prompt_nu...
mit
dietmarw/EK5312_ElectricalMachines
Chapman/Ch5-Problem_5-10.ipynb
1
4480
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Excercises Electric Machinery Fundamentals\n", "## Chapter 5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Problem 5-10" ] }, { "cell_type": "code", "execution_count": 1, "me...
unlicense
mne-tools/mne-tools.github.io
0.21/_downloads/9cb26d39ca23b6aac4c0d201a4775849/plot_brainstorm_data.ipynb
1
3233
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Brainstorm...
bsd-3-clause
ramabrahma/data-sci-int-capstone
.ipynb_checkpoints/data-exploration-life-insurance-checkpoint.ipynb
1
311728
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploration of Prudential Life Insurance Data\n", "\n", "### Data retrieved from: \n", "https://www.kaggle.com/c/prudential-life-insurance-assessment\n", "\n", "\n", "###### File descriptions:\n", "\n", ...
gpl-3.0
g-weatherill/notebooks
gmpe-smtk/Ground Motion IMs Short.ipynb
1
18234
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Calculating Ground Motion Intensity Measures" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The SMTK contains two modules for the characterisation of ground motion:\n", "\n", "1) smtk.respon...
agpl-3.0
jasti/CrossfitAnalyser
CrossfitAnalyser.ipynb
1
89774
{ "metadata": { "name": "", "signature": "sha256:031d4f09d4428b6bbea15479ea61ffc68f20a563c263a7ec4aebd375631d052e" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "import csv\n"...
mit
bps90/bps90.github.io
assets/files/papers/7/WCNC-2016/.ipynb_checkpoints/ParseEdgeList-checkpoint.ipynb
1
3165
{ "metadata": { "name": "", "signature": "sha256:ff125a7e55001c106f8fbc3a423fce14d3050550a263c5c1554c142a03638144" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from random import randrange\n", "fro...
mit
scraperwiki/databaker
databaker/tutorial/Finding_your_way.ipynb
2
53859
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Opening and previewing\n", "\n", "This uses the tiny excel spreadsheet example1.xls. It is small enough to preview inline in this notebook. But for bigger spreadsheet tables you will want to open them up in a separate windo...
agpl-3.0
NazBen/impact-of-dependence
notebooks/grid-search.ipynb
1
696987
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Conservative Estimation using a Grid Seach Minimization\n", "\n", "This notebook illustrates the different steps for a conservative estimation using a grid search minimization.\n", "\n", "###### Classic Libraries" ...
mit
Disiok/poetry-seq2seq
notebooks/Vera's Experiments.ipynb
1
3061
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n", "Building prefix dict from the default dictionary ...\n", "Loading model from cache /tmp/jieba....
mit
pioneers/topgear
robot.ipynb
1
9751
{ "metadata": { "name": "", "signature": "sha256:9960c86d79cec218abef84e916a5e727efaacadf81c4943fb7249106b3e9ad05" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"http://mirageforum.com/forum/at...
apache-2.0
mne-tools/mne-tools.github.io
stable/_downloads/bcaf3ed1f43ea7377c6c0b00137d728f/custom_inverse_solver.ipynb
1
8476
{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Source loc...
bsd-3-clause
beardeer/playground
notebooks/clustering.ipynb
1
994939
{ "cells": [ { "cell_type": "code", "execution_count": 674, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import csv\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import sklearn.cluster as cluster\n", "impo...
unlicense
google/or-tools
examples/notebook/contrib/car.ipynb
1
6839
{ "cells": [ { "cell_type": "markdown", "id": "google", "metadata": {}, "source": [ "##### Copyright 2021 Google LLC." ] }, { "cell_type": "markdown", "id": "apache", "metadata": {}, "source": [ "Licensed under the Apache License, Version 2.0 (the \"License\");\n", "you may...
apache-2.0
nens/python-subgrid
notebooks/slicing.ipynb
1
152633
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, { ...
gpl-3.0
ijmbarr/causalgraphicalmodels
notebooks/cgm-examples.ipynb
1
71992
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# An Introduction to `CausalGraphicalModels`\n", "\n", "`CausalGraphicalModel` is a python module for describing and manipulating [Causal Graphical Models](https://en.wikipedia.org/wiki/Causal_graph) and [Structural Causal Mode...
mit
kimkipyo/dss_git_kkp
통계, 머신러닝 복습/160628화_22일차_서포트 벡터 머신_SVM_Support Vector Machine/1.서포트 벡터 머신.ipynb
1
1627158
null
mit
vgm64/highway-radio
ExploreData.ipynb
1
138524
{ "metadata": { "name": "", "signature": "sha256:f9f0a366b19f8b11ea650f7e59ab88e2576446fa890c09b4a01f77cc5010334d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import MySQLdb\n", "import numpy as n...
mit
bearing/dosenet-analysis
Weather Station Data Query.ipynb
1
205107
{ "cells": [ { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [ { "data": { "text/html": [ "\n", " <div class=\"bk-root\">\n", " <a href=\"https://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-small bk-logo-notebook\"></...
mit
socallaghan/linearRegressionTutorial
linearRegressionAnswers.ipynb
1
470629
{ "metadata": { "name": "", "signature": "sha256:bacb24223678dc058d06ddfd171c59d9668b383046d7614166b3bf5f89e31dc3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "<center> Linear Regression...
gpl-2.0
Unidata/unidata-python-workshop
notebooks/MetPy_Case_Study/MetPy_Case_Study.ipynb
1
52647
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a name=\"top\"></a>\n", "<div style=\"width:1000 px\">\n", "\n", "<div style=\"float:right; width:98 px; height:98px;\">\n", "<img src=\"https://raw.githubusercontent.com/Unidata/MetPy/master/src/metpy/plots/_static/un...
mit
letsgoexploring/economicData
business-cycle-data/python/.ipynb_checkpoints/business_cycle_data-checkpoint.ipynb
1
29859
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# U.S. Business Cycle Data\n", "\n", "This notebook downloads, manages, and exports several data series for studying business cycles in the US. Four files are created in the `csv` directory:\n", "\n", "File name ...
mit
dmittov/misc
BikeSharing-Linear.ipynb
1
36202
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Linear methods" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "https://www.kaggle.com/c/bike-sharing-demand" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outp...
apache-2.0
m-labs/artiq
artiq/examples/artiq_ipython_notebook.ipynb
1
24533
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pyla...
lgpl-3.0
erccarls/vectorsearch
notebooks/04 - doc2vec- training.ipynb
1
28620
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " business_id date review_id stars \\\n", "10 UsFtqoBl7naz8AVUBZMjQQ...
apache-2.0
CullenGao/LSTM_PittsRoutine
script/.ipynb_checkpoints/smooth_theta-checkpoint.ipynb
1
13832
{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "3644\n" ] } ], "source": [ "# HEADS-UP: using inline to display instead of plt.show() every...
mit
shanot/imp
modules/pmi/examples/analysis/analyse_crosslink_from_a_cluster.ipynb
2
1576
{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This script when it is run in a clustering directory" ] }, { "cell_type": "code", "collapsed": false, "i...
gpl-3.0
shantnu/WebScraping
Ipython/Selenium_4.ipynb
1
3713
{ "metadata": { "name": "", "signature": "sha256:adfae323dc758a930b3d9fc30a92f490f930dafbdd26b6688020a3c01e3b13be" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from selenium import webdriver\n", "f...
mit
Caoimhinmg/PmagPy
data_files/Essentials_Examples/Notebooks/.ipynb_checkpoints/essentials_ps_1_template-checkpoint.ipynb
1
5495
{ "metadata": { "name": "", "signature": "sha256:a6165acf97d4696c9378c9b99ebc3b28b834325b1eb95f1053b9b2995e733a8b" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "IPython Notebook for turni...
bsd-3-clause
proto-n/Alpenglow
examples/external_models/libfm/evaluate.ipynb
2
143165
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import sys\n", "import shutil\n", "from alpenglow.experiments import BatchFactorExperiment, ExternalModelExperiment\n", "from a...
apache-2.0
linsalrob/EdwardsLab
jupyter/liz_spreadsheets.ipynb
1
71960
{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n...
mit
qxcv/joint-regressor
keras/flow-rgb-graph-net-surgery-poselet.ipynb
1
138269
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Net surgery to produce a `Graph` model for regressing poselets" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n"...
apache-2.0
magenta/magenta-demos
colab-notebooks/MusicXML_Document_Structure_Documentation.ipynb
1
6164017
null
apache-2.0
psygrammer/coco
part3/bayes/ch14/Baye_Chap14.ipynb
1
1034287
{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 14. Multinominal processing trees\n", "\n", " ## 14.1 Multinomial processing model of pair-clustering\n", " \n", " The finding that semantically related items are often recalled consecutively can be taken as evidence ...
mit
billzhao1990/CS231n-Spring-2017
assignment2/BatchNormalization.ipynb
1
222781
{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Batch Normalization\n", "One way to make deep networks easier to train is to use more sophisticated optimization procedures such as SGD+momentum, RMSProp, or Adam. Another strategy ...
mit