codeparrot/github-jupyter · Datasets at Hugging Face

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dsacademybr/PythonFundamentos	Cap04/Notebooks/DSA-Python-Cap04-Exercicios-Solucao.ipynb	1	8978	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 4</font>\n", "\n", "## Download: http://github.com/dsacademybr" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "out...	gpl-3.0
mne-tools/mne-tools.github.io	0.20/_downloads/05c57a644672d33707fd1264df7f5617/plot_time_frequency_global_field_power.ipynb	1	7227	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Explore ev...	bsd-3-clause
daviddesancho/mdtraj	examples/WebGL-Viewer.ipynb	4	3145	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Interactive WebGL trajectory widget\n", "\n", "Note: this feature requires a 'running' notebook, connected to a live kernel. It will not work with a staticly rendered display. For an introduction to the IPython interactive w...	lgpl-2.1
ngcm/training-public	FEEG6016 Simulation and Modelling/07a-Finite-Elements-Lab-1a-50-lines.ipynb	1	285843	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Finite Elements in 50 lines\n", "\n", "Taken from the Matlab code ([see this link](https://www.particleincell.com/2012/matlab-fem/))." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colla...	mit
huseinzol05/Deep-Learning-Tensorflow	Regression/elasticnet-regression-tensorflow.ipynb	1	77311	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dat...	mit
arnoldlu/lisa	ipynb/sched_dvfs/smoke_test.ipynb	5	1064204	null	apache-2.0
ajhenrikson/phys202-2015-work	assignments/assignment03/NumpyEx03.ipynb	1	67434	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Numpy Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata"...	mit
davidjaimes/ncat	notebooks/Jupyter-tutorial.ipynb	2	62328	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAggAAABDCAYAAAD5/P3lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAH3AAAB9wBYvxo6AAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAACAA...	unlicense
BL-Labs/meetingsparser	refining add_meetingdays_to_csv.py.ipynb	1	1448	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "refining add_meetingdays_to_csv.py\n", "\n", "currently, the code does 1 and 3 of the following:\n", "\n", "1 - a single match is found (ie one of the day matches) = daysfound.csv\n", "2 - more than one match is fou...	mit
ritviksahajpal/open-geo-tutorial	Python/chapters/chapter_4_vector.ipynb	1	26535	{ "metadata": { "name": "", "signature": "sha256:683dcbf6525dd1cdbe96018dfbbf480acfcf12a98c5778118768bbb97a3793ac" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Chris Holden (ceholden@gmail.com) - https:...	gpl-2.0
kgourgou/stochastic-simulations-class	ipython_notebooks/langevin.ipynb	1	541386	{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "<script>\n", " function code_toggle() {\n", " if (code_shown){\n", " $('div.input').hide('500');\n", " $('#toggleButton').val('Show Code')\n", " } else {\n", " $('div.input').show('500');\n", ...	mit
prabhamatta/Analyzing-Open-Data	notebooks/Day_13_C_Baby_Names_MF_assignment.ipynb	1	1330809	null	apache-2.0
ashwindeo/dataschoolers	2_ProcessBookHiLoGradRate.ipynb	1	9431081	null	apache-2.0
modin-project/modin	examples/tutorial/jupyter/execution/omnisci_on_native/local/exercise_1.ipynb	1	7111	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![LOGO](../../../img/MODIN_ver2_hrz.png)\n", "\n", "<center><h2>Scale your pandas workflows by changing one line of code</h2>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 1: How...	apache-2.0
idc9/law-net	explore/Iain/pref_attach/PageRank_DAG.ipynb	1	124425	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Investigate PageRank in a DAG" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ...	mit
abhipr1/DATA_SCIENCE_INTENSIVE	Week_1/DATA_WRANGLING/WORKING_WITH_DATA_IN_FILES/data_wrangling_xml/data_wrangling_xml/.ipynb_checkpoints/sliderule_dsi_xml_exercise-checkpoint.ipynb	1	32308	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XML example and exercise\n", "**\n", "+ study examples of accessing nodes in XML tree structure \n", "+ work on exercise to be completed and submitted\n", "**\n", "+ reference: https://docs.python.org/2.7/lib...	apache-2.0
4dsolutions/Python5	About_Decorators.ipynb	1	5011	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Decorators\n", "\n", "![UFO](https://i.ytimg.com/vi/zUFtNLeUXgU/maxresdefault.jpg)\n", "\n", "\n", "I use UFO as a decorator not because I want or need people to believe in UFOs, but because the science fiction id...	mit
rhlobo/playground	ipy-notebooks/regex - Parsing multiple groups.ipynb	1	5571	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import re" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cel...	mit
sebastiandres/mat281	laboratorios/lab01-PythonNumerico/PythonNumerico.ipynb	2	43643	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<header class=\"w3-container w3-teal\">\n", "<img src=\"images/utfsm.png\" alt=\"\" height=\"100px\" align=\"left\"/>\n", "<img src=\"images/mat.png\" alt=\"\" height=\"10...	cc0-1.0
melissawm/lpwithnotebooks	exemplo/IDEB.ipynb	1	78027	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Exemplo: Análise do IDEB" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Neste notebook, vamos analisar dados relativos ao IDEB calculado por munic...	gpl-3.0
jadijadi/persianlettercount	persian_letter_count.ipynb	1	162551	{ "metadata": { "name": "", "signature": "sha256:6fdfe924b976dfe69ed19141a307fbb6fcd0a099b51f5bcfc56731b03e5a22e4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Distribution of persian letters\n", ...	gpl-2.0
ethen8181/machine-learning	networkx/max_influence/max_influence.ipynb	1	162670	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Submodular-Optimization-&-Influence-Maximization\" data-toc-modified-id=\"Submodu...	mit
jwjohnson314/data-803	notebooks/Regularization and Model Tuning.ipynb	1	506439	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Regularization\n", "Regularization is the name for a technique developed at different times and in different ways in statistics and machine learning for improving the predictive quality of a model. The idea is to make a model sim...	mit
othersite/document	machinelearning/deep-learning-book/code/model_zoo/multilayer-perceptron-dropout.ipynb	1	17923	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "*Accompanying code examples of the book \"Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python\" by [Sebastian Raschka](https://sebastianraschka.com). All code examples are release...	apache-2.0
InsightLab/data-science-cookbook	2019/02-python-bibliotecas-manipulacao-dados/pandas_basico.ipynb	3	31111	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importando o Pandas e o NumPy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, ...	mit
gaufung/Data_Analytics_Learning_Note	Data_Analytics_in_Action/pandasIO.ipynb	1	68175	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas 数据读写" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# API\n", "读取 \| 写入 \n", "--- \| ---\n", "read_csv \| to_csv\n", "read_excel \| to_excel\n", "read_...	mit
yskmurakami/MPMaximizer	MPMiximizer.ipynb	1	20625	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "A = np.array(\n", "[[4.0, 0.0],\n", " [3.0, 2.0]]\n", ")\n", "\n", "B = np.array(\n", "[[ 7.0, 0....	mit
SergioSantGre/project_portfolio	document_similarity2.ipynb	1	6273504	null	lgpl-3.0
jljones/portfolio	ds/Webscraping_Craigslist_multi.ipynb	1	285850	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Webscraping Craigslist for Housing Listings in the East Bay\n", "\n", "### Jennifer Jones" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { ...	apache-2.0
basnijholt/holoviews	examples/reference/elements/bokeh/TriMesh.ipynb	1	5623	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n", "<dl class=\"dl-horizontal\">\n", " <dt>Title</dt> <dd> TriMesh Element</dd>\n", " <dt>Dependencies</dt> <dd>Bokeh</dd>\n", " <dt>Backends</...	bsd-3-clause
awni/tensorflow	tensorflow/tools/docker/notebooks/2_getting_started.ipynb	1	147269	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "version": "0.3.2", "views": {}, "default_view": {}, "name": "Untitled", "provenance": [] } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "6TuWv0Y0sY8n", "colab_t...	apache-2.0
kaushik94/tardis	docs/research/code_comparison/plasma_compare/plasma_compare.ipynb	2	74046	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Plasma comparison ###" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/wkerzend/miniconda/env...	bsd-3-clause
kriukov/interval-methods	ipynb/asin-extension.ipynb	1	388777	{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Resolution of the asin() problemResolution of the asin() problem" ] }, { "cell_type": "mark...	gpl-3.0
neurodata/ndmg	tutorials/Overview.ipynb	1	22964	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ndmg Tutorial: Running Inside Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial provides a basic overview of how to run ndmg manually within Python. <br>\n", "We begin by check...	apache-2.0
mirjalil/DataScience	algorithms-in-C++/data-structures_00_overview.ipynb	1	769	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Data Structures in C++\n", "=======\n", "\n", "## C++ STL\n", "\n", "Containers: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [],...	gpl-2.0
ahwkuepper/stdme	code/app_model_chlamydia.ipynb	1	92068	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A first simple model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import pickle\n", "import numpy ...	mit
dseuss/notebooks	Ellipsoid Method.ipynb	1	287207	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import cdd\n", "import itertools as it\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "from matplo...	unlicense
basnijholt/holoviews	examples/reference/containers/plotly/Overlay.ipynb	1	2816	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n", "<dl class=\"dl-horizontal\">\n", " <dt>Title</dt> <dd>Overlay Container</dd>\n", " <dt>Dependencies</dt> <dd>Plotly</dd>\n", " <dt>Backends...	bsd-3-clause
Ccaccia73/semimonocoque	01_SemiMonoCoque.ipynb	1	215557	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Semi-Monocoque Theory" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/hom...	mit
thammegowda/notes	usc-csci-ml/hw4/src/CSCI567_hw4_fall16.ipynb	2	32928	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "from hw_utils import *\n", "from copy impo...	apache-2.0
tensorflow/neural-structured-learning	workshops/kdd_2020/adversarial_regularization_mnist.ipynb	1	45218	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "375gw63MC9Hg" }, "source": [ "##### Copyright 2020 Google LLC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, ...	apache-2.0
elsonidoq/py-l1tf	l1tf/Quick Example.ipynb	1	167463	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: nbAgg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "sourc...	apache-2.0
taroplus/sparkling-notebook	notebooks/Matplotlib Example.ipynb	2	18884	{ "cells" : [ { "metadata" : { "trusted" : true, "collapsed" : false, "editable" : true, "deletable" : true }, "cell_type" : "code", "source" : [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "t = np.arange(0.0, 2.0, 0.01)\n", "s = 1 + np.sin(2np.pit)\n",...	apache-2.0
olafplacha/OpenCV-intro	feature matching.ipynb	1	1851179	null	mit
wcmckee/garrison-wow-track	Untitled.ipynb	1	556	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "...	mit
bgruening/galaxy-ipython	templates/notebook.ipynb	1	1083	{ "metadata": { "name": "", "signature": "sha256:%s" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Welcome to the interactive Galaxy IPython Notebook." ] }, { "cell_type...	mit
4DGenome/Chromosomal-Conformation-Course	Participants/deo/00_fastq_QC.ipynb	1	240339	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "00_fastq_QC.ipynb learning_python.ipynb\r\n" ] } ], "source": [ "!ls" ] }, { "ce...	gpl-3.0
ehsteve/ipython-notebooks	RHESSI Workshope 13 - SunPy.ipynb	2	1006964	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"https://raw.github.com/sunpy/sunpy-logo/master/generated/sunpy_logo_compact_192x239.png\">\n", "# SunPy Demo RHESSI...	bsd-3-clause
di-br/CalMAdju	doc/metric_tests/Gradients.ipynb	1	541682	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Import required modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "...	gpl-3.0
google/starthinker	colabs/google_api_to_bigquery.ipynb	1	9224	{ "license": "Licensed under the Apache License, Version 2.0", "copyright": "Copyright 2020 Google LLC", "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Google API To BigQuery", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernel...	apache-2.0
boya-zhou/kaggle_bimbo_reformat	notebooks/5_random_forest.ipynb	1	22221	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Random_forest_regressor\n", "# extra_tree_regressor\n", "# sklearn.svm.SVR" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { ...	mit
keiikegami/envelopetheorem	ほうらくせん.ipynb	1	181033	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -- coding: utf-8 --" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ ...	bsd-3-clause
tpin3694/tpin3694.github.io	machine-learning/f1_score.ipynb	2	3004	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: F1 Score \n", "Slug: f1_score \n", "Summary: How to evaluate a Python machine learning using F1 score. \n", "Date: 2017-09-15 12:00 \n", "Category: Machine Learning \n", "Tags: Model Evaluation\n", ...	mit
SylvainCorlay/ipywidgets	docs/source/examples/Index.ipynb	1	2451	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Back to the main [Index](../Index.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython ...	bsd-3-clause
SylvainCorlay/bqplot	examples/Index.ipynb	1	7017	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# bqplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`bqplot` is a [Grammar of Graphics](https://www.cs.uic.edu/~wilkinson/TheGrammarOfGraphics/GOG.html) based interactive plotting framework for th...	apache-2.0
QuantStack/quantstack-talks	2020-01-09-PyData-Heidelberg/examples/gradient_descent.ipynb	1	5397	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook served by Voilà" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Notebook copied from https://github.com/ChakriCherukuri/mlviz" ] }, { "cell_type": "markdown", "metadata": {...	bsd-3-clause
savioabuga/arrows	arrows.ipynb	2	763487	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# arrows: Yet Another Twitter/Python Data Analysis\n", "## Geospatially, Temporally, and Linguistically Analyzing Tweets about Top U.S. Presidential Candidates with Pandas, TextBlob, Seaborn, and Cartopy\n", "\n", "Hi, I'm ...	mit
flaviocordova/udacity_deep_learn_project	gan_mnist/Intro_to_GANs_Solution.ipynb	1	218986	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generative Adversarial Network\n", "\n", "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n", "\n", ...	mit
kit-cel/wt	sigNT/tutorial/approximation.ipynb	2	46483	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Content and Objective\n", "\n", "+ Show approximations by using gaussian approximation\n", "+ Additionally, applying Gram-Schmidt for \"orthonormalizing\" a set of functions" ] }, { "cell_type": "code", "exec...	gpl-2.0
djvanhelmond/AdventofCode2015	Advent of Code - Puzzle Selector.ipynb	1	1930	{ "cells": [ { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#!/usr/bin/env python3\n", "import random" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs...	mit
kuroniko/MAT258	258a_hw4.ipynb	1	46154	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MAT 258A: Homework 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 4\n", "In this exercise, I test the code of 'pure' Newton (newtmin), Newton method with Armijo backtracking (newtmi...	mit
dhuppenkothen/ShiftyLines	notebooks/ResponsesTest.ipynb	1	4248010	null	gpl-3.0
jsjol/GaussianProcessRegressionForDiffusionMRI	notebooks/show_ODFs.ipynb	1	9064	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import os\n", "import sys\n", "module_path = os.path.abspath(os.path.join('..'))\n", "if modul...	bsd-3-clause
esa-as/2016-ml-contest	ar4/ar4_submission2_VALIDATION.ipynb	2	106501	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Facies classification using machine learning techniques\n", "Copy of <a href=\"https://home.deib.polimi.it/bestagini/\">Paolo Bestagini's</a> \"Try 2\", augmented, by Alan Richardson (Ausar Geophysical), with an ML estimator for ...	apache-2.0
nicolas998/wmf	Examples/Ejemplo_Hidrologia_Maximos.ipynb	1	595267	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Realiza el análisis hidrológico de la cuenca de Danta\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", ...	gpl-3.0
PlanetExp/rcnn	rcnn_session.ipynb	1	24022	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- - - - - - - - - - - - - - - - - - - - \n", "Loaded dataset data/grids_9x9_1000.hdf5\n", "X.dtyp...	apache-2.0
csunny/blog_project	source/libs/analysis/pandas/practise.ipynb	1	31442	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 7, "metadata": ...	mit
julienchastang/unidata-python-workshop	notebooks/Python_Ecosystem/Scientific_Python_Ecosystem_Overview.ipynb	1	8695	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The Scientific Python Ecosystem\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python\n", "\n", "Python is a interpreted, high-level programming language that is meant to be e...	mit
MaxPowerWasTaken/MaxPowerWasTaken.github.io	jupyter_notebooks/superseded/Multiprocessing in Pandas minimal.ipynb	2	1833	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Easy Multiprocessing on Pandas DataFrames" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python's `Pandas` library is great for all sorts of data-wrangling tasks. What doesn't come out of the box wi...	gpl-3.0
alex-ip/geophys_utils	examples/10_gravity_point_discovery_and_access_demo.ipynb	1	5378811	null	apache-2.0
QuantStack/quantstack-talks	2018-08-23-jupytercon-native/notebooks/xframe.ipynb	1	6900	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![xframe](../src/xframe.svg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<center> <h1>xframe is a dataframe for C++, based on xtensor and xtl</h1> </center>" ] }, { "cell_type": "code", ...	bsd-3-clause
intel-analytics/analytics-zoo	docs/docs/colab-notebook/orca/examples/fashion_mnist_bigdl.ipynb	1	14788611	null	apache-2.0
EderSantana/blocks_contrib	tests/DelayLine.ipynb	1	85859	{ "metadata": { "name": "", "signature": "sha256:e1c45b0ff2c887a302d3c3841e0f9ac78f090f38f5e81b6c6ae1f689014d25b2" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from fuel.datasets import Dataset\n", ...	mit
imprm/nummet_I	ComputerArithmeticExamples.ipynb	1	14275	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "ESE - Numerical Methods I: Basics of Computer Arithmetic - Examples" ] }, { "cel...	mit
atulsingh0/MachineLearning	python_DC/ST_Python_02a.ipynb	1	355141	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistical Thinking in Python (Part 2)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import\n", "import pandas as pd\n", ...	gpl-3.0
ibmkendrick/streamsx.health	samples/HealthcareJupyterDemo/notebooks/experimental/Create VCAP Service Credential.ipynb	3	1845	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Create `vcap_services.json` to capture Bluemix VCAP service credential\n", "\n", "Usage:\n", "* In [Bluemix](http://bluemix.net), create `Streaming Analytics` service\n", "* Open the `Streaming Analytics` service\n", ...	apache-2.0
nehal96/Deep-Learning-ND-Exercises	Sentiment Analysis/Handwritten Digit Recognition with TFLearn and MNIST/handwritten-digit-recognition-with-tflearn.ipynb	1	29680	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Handwritten Number Recognition with TFLearn and MNIST\n", "\n", "In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9. \n", "\n", "This kind of neural network is used in a varie...	mit
gregorjerse/rt2	2015_2016/lab13/Extending values on vertices.ipynb	1	6863	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Extending values on vertices to a discrete gradient vector field\n", "During extension algorithm one has to compute lover_link for every vertex in the complex. So let us implement search for the lower link first. It requires qui...	gpl-3.0
WNoxchi/Kaukasos	FADL1/importfastaitest.ipynb	1	1000	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from fastai.imports import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "tex...	mit
pioneers/topgear	coroutine_spin_test.ipynb	1	16679	{ "metadata": { "name": "", "signature": "sha256:246de87d1823dcfd28158f479ebeba5932bd42b37de3a5679728fa970c2fcc5c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"http://mirageforum.com/forum/at...	apache-2.0
DJCordhose/ai	notebooks/workshops/tss/cnn-standard-architectures.ipynb	2	108858	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Training on an Advanced Standard CNN Architecture\n", "* https://keras.io/applications/\n", "* The 9 Deep Learning Papers You Need To Know About: https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-...	mit
santanche/java2learn	notebooks/pt/c05exercicios/s02resolucoes/small-challenges-03.ipynb	1	4703	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Small Challenges 3\n", "\n", "Dadas as seguintes classes:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "public class CA {\n", " public String t...	gpl-2.0
srcole/qwm	misc/Sawtooth PAC.ipynb	1	159245	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A sawtooth wave has statistical phase-amplitude coupling\n", "\n", "This notebook is to show that a sawtooth wave will generate spurious phase-amplitude coupling, particularly at high frequencies (i.e. above 20-60Hz)\n", ...	mit
philipwangdk/HPC	uwhpsc/labs/lab17/Tridiagonal.ipynb	2	16008	{ "metadata": { "name": "", "signature": "sha256:03891f69e60e29f26299bb857e71dcb46c7d215cad80ca995116987e2a0eeb44" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solving tridiagonal systems using scipy....	mit
bajorekp/ForexPredictor	Mnist.ipynb	1	3657	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting /tmp/data/train-images...	mit
surchs/Logbooks	rescue_comp.ipynb	1	1629484	null	gpl-3.0
jakevdp/data-CDCbirths	BirthsByDay.ipynb	1	61837	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# USA Births Data\n", "\n", "By Jake VanderPlas. See http://github.com/jakevdp/data-CDCbirths/\n", "\n", "This dataset records birth rates in the USA by year. It was compiled from data on the [CDC website](http://www....	bsd-3-clause
darioizzo/d-CGP	doc/sphinx/notebooks/symbolic_regression_3.ipynb	1	76709	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi-objective memetic approach\n", "\n", "In this third tutorial we consider an example with two dimensional input data and we approach its solution using a multi-objective approach where, aside the loss, we consider the fo...	gpl-3.0
patrick-kidger/equinox	examples/init_apply.ipynb	1	3057	{ "cells": [ { "cell_type": "markdown", "id": "14148da3-23fb-480b-ae31-93878cda86fa", "metadata": {}, "source": [ "# Compatibility with init-apply libraries\n", "\n", "Existing JAX neural network libraries have sometimes followed the \"init/apply\" approach, in which the parameters of a netwo...	apache-2.0
aitatanit/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers	Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb	1	1153635	null	mit
mercybenzaquen/foundations-homework	foundations_hw/08/Homework8_benzaquen_congress_data.ipynb	2	271784	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied (use --upgrade to upgrade): pandas in /Users/mercybenzaquen/.virtualenvs/Homewor...	mit
keiikegami/theano	MLE.ipynb	2	2615	{ "cells": [ { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "theta : [ 4.05360992 1.95336957]\n", "True theta: [4, 2]\n", "sigma: 1.3160388223\n", "T...	mit
phoebe-project/phoebe2-docs	development/tutorials/datasets_advanced.ipynb	2	17640	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Advanced: Datasets\n", "============================\n", "\n", "Datasets tell PHOEBE how and at what times to compute the model. In some cases these will include the actual observational data, and in other cases may only i...	gpl-3.0
opalbert/mltutorial	notebooks/data-mining/1. Web Scraping.ipynb	4	192214	{ "metadata": { "name": "", "signature": "sha256:bda576f544c5c3c928cf09474fc234c8a15aebe832608fcd49802d53914e2869" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Tutorial Brief" ] ...	gpl-2.0
neurotechuoft/MindType	Code/V1/EEG_Channel_Exploration.ipynb	1	5526460	null	agpl-3.0
qinwf-nuan/keras-js	notebooks/layers/wrappers/TimeDistributed.ipynb	1	16195	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "from keras.models import Model\n", "f...	mit
catalystcomputing/DSIoT-Python-sessions	Session201811/code/02 Python Assignment.ipynb	1	1845	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Strings\n", "data = 'Hello World'\n", "print data[0]\n", ...	apache-2.0
tuanavu/coursera-university-of-washington	machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb	2	443945	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Training Logistic Regression via Stochastic Gradient Ascent\n", "\n", "The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:\n", "\n", " * Extract featu...	mit
jegibbs/phys202-2015-work	assignments/assignment11/OptimizationEx01.ipynb	1	21572	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Optimization Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "me...	mit
jgarciab/Stats_Python	Statistics 2014-09-29.ipynb	1	482112	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": true, "input": [ "__author__ = 'Javier Garcia-Bernardo 2014'\n", "\n", "# Math\n", "import numpy as np\n", "\n", "# Stats\...	agpl-3.0

dsacademybr/PythonFundamentos

Cap04/Notebooks/DSA-Python-Cap04-Exercicios-Solucao.ipynb

1

8978

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 4</font>\n", "\n", "## Download: http://github.com/dsacademybr" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "out...

gpl-3.0

mne-tools/mne-tools.github.io

0.20/_downloads/05c57a644672d33707fd1264df7f5617/plot_time_frequency_global_field_power.ipynb

1

7227

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

bsd-3-clause

daviddesancho/mdtraj

examples/WebGL-Viewer.ipynb

4

3145

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Interactive WebGL trajectory widget\n", "\n", "Note: this feature requires a 'running' notebook, connected to a live kernel. It will not work with a staticly rendered display. For an introduction to the IPython interactive w...

lgpl-2.1

ngcm/training-public

FEEG6016 Simulation and Modelling/07a-Finite-Elements-Lab-1a-50-lines.ipynb

1

285843

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Finite Elements in 50 lines\n", "\n", "Taken from the Matlab code ([see this link](https://www.particleincell.com/2012/matlab-fem/))." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "colla...

mit

huseinzol05/Deep-Learning-Tensorflow

Regression/elasticnet-regression-tensorflow.ipynb

1

77311

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div>\n", "<style>\n", " .dataframe thead tr:only-child th {\n", " text-align: right;\n", " }\n", "\n", " .dat...

mit

arnoldlu/lisa

ipynb/sched_dvfs/smoke_test.ipynb

5

1064204

null

apache-2.0

ajhenrikson/phys202-2015-work

assignments/assignment03/NumpyEx03.ipynb

1

67434

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Numpy Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata"...

mit

davidjaimes/ncat

notebooks/Jupyter-tutorial.ipynb

2

62328

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAggAAABDCAYAAAD5/P3lAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAAH3AAAB9wBYvxo6AAAABl0RVh0U29mdHdhcmUAd3d3Lmlua3NjYXBlLm9yZ5vuPBoAACAA...

unlicense

BL-Labs/meetingsparser

refining add_meetingdays_to_csv.py.ipynb

1

1448

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "refining add_meetingdays_to_csv.py\n", "\n", "currently, the code does 1 and 3 of the following:\n", "\n", "1 - a single match is found (ie one of the day matches) = daysfound.csv\n", "2 - more than one match is fou...

mit

ritviksahajpal/open-geo-tutorial

Python/chapters/chapter_4_vector.ipynb

1

26535

{ "metadata": { "name": "", "signature": "sha256:683dcbf6525dd1cdbe96018dfbbf480acfcf12a98c5778118768bbb97a3793ac" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Chris Holden (ceholden@gmail.com) - https:...

gpl-2.0

kgourgou/stochastic-simulations-class

ipython_notebooks/langevin.ipynb

1

541386

{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "<script>\n", " function code_toggle() {\n", " if (code_shown){\n", " $('div.input').hide('500');\n", " $('#toggleButton').val('Show Code')\n", " } else {\n", " $('div.input').show('500');\n", ...

mit

prabhamatta/Analyzing-Open-Data

notebooks/Day_13_C_Baby_Names_MF_assignment.ipynb

1

1330809

null

apache-2.0

ashwindeo/dataschoolers

2_ProcessBookHiLoGradRate.ipynb

1

9431081

null

apache-2.0

modin-project/modin

examples/tutorial/jupyter/execution/omnisci_on_native/local/exercise_1.ipynb

1

7111

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![LOGO](../../../img/MODIN_ver2_hrz.png)\n", "\n", "<center><h2>Scale your pandas workflows by changing one line of code</h2>\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 1: How...

apache-2.0

idc9/law-net

explore/Iain/pref_attach/PageRank_DAG.ipynb

1

124425

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Investigate PageRank in a DAG" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ...

mit

abhipr1/DATA_SCIENCE_INTENSIVE

Week_1/DATA_WRANGLING/WORKING_WITH_DATA_IN_FILES/data_wrangling_xml/data_wrangling_xml/.ipynb_checkpoints/sliderule_dsi_xml_exercise-checkpoint.ipynb

1

32308

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XML example and exercise\n", "****\n", "+ study examples of accessing nodes in XML tree structure \n", "+ work on exercise to be completed and submitted\n", "****\n", "+ reference: https://docs.python.org/2.7/lib...

apache-2.0

4dsolutions/Python5

About_Decorators.ipynb

1

5011

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Decorators\n", "\n", "![UFO](https://i.ytimg.com/vi/zUFtNLeUXgU/maxresdefault.jpg)\n", "\n", "\n", "I use UFO as a decorator not because I want or need people to believe in UFOs, but because the science fiction id...

mit

rhlobo/playground

ipy-notebooks/regex - Parsing multiple groups.ipynb

1

5571

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import re" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 4 }, { "cel...

mit

sebastiandres/mat281

laboratorios/lab01-PythonNumerico/PythonNumerico.ipynb

2

43643

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "<header class=\"w3-container w3-teal\">\n", "<img src=\"images/utfsm.png\" alt=\"\" height=\"100px\" align=\"left\"/>\n", "<img src=\"images/mat.png\" alt=\"\" height=\"10...

cc0-1.0

melissawm/lpwithnotebooks

exemplo/IDEB.ipynb

1

78027

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Exemplo: Análise do IDEB" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Neste notebook, vamos analisar dados relativos ao IDEB calculado por munic...

gpl-3.0

jadijadi/persianlettercount

persian_letter_count.ipynb

1

162551

{ "metadata": { "name": "", "signature": "sha256:6fdfe924b976dfe69ed19141a307fbb6fcd0a099b51f5bcfc56731b03e5a22e4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Distribution of persian letters\n", ...

gpl-2.0

ethen8181/machine-learning

networkx/max_influence/max_influence.ipynb

1

162670

{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n", "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Submodular-Optimization-&-Influence-Maximization\" data-toc-modified-id=\"Submodu...

mit

jwjohnson314/data-803

notebooks/Regularization and Model Tuning.ipynb

1

506439

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Regularization\n", "Regularization is the name for a technique developed at different times and in different ways in statistics and machine learning for improving the predictive quality of a model. The idea is to make a model sim...

mit

othersite/document

machinelearning/deep-learning-book/code/model_zoo/multilayer-perceptron-dropout.ipynb

1

17923

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "*Accompanying code examples of the book \"Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python\" by [Sebastian Raschka](https://sebastianraschka.com). All code examples are release...

apache-2.0

InsightLab/data-science-cookbook

2019/02-python-bibliotecas-manipulacao-dados/pandas_basico.ipynb

3

31111

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Importando o Pandas e o NumPy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, ...

mit

gaufung/Data_Analytics_Learning_Note

Data_Analytics_in_Action/pandasIO.ipynb

1

68175

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Pandas 数据读写" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# API\n", "读取 | 写入 \n", "--- | ---\n", "read_csv | to_csv\n", "read_excel | to_excel\n", "read_...

mit

yskmurakami/MPMaximizer

MPMiximizer.ipynb

1

20625

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "A = np.array(\n", "[[4.0, 0.0],\n", " [3.0, 2.0]]\n", ")\n", "\n", "B = np.array(\n", "[[ 7.0, 0....

mit

SergioSantGre/project_portfolio

document_similarity2.ipynb

1

6273504

null

lgpl-3.0

jljones/portfolio

ds/Webscraping_Craigslist_multi.ipynb

1

285850

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Webscraping Craigslist for Housing Listings in the East Bay\n", "\n", "### Jennifer Jones" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { ...

apache-2.0

basnijholt/holoviews

examples/reference/elements/bokeh/TriMesh.ipynb

1

5623

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n", "<dl class=\"dl-horizontal\">\n", " <dt>Title</dt> <dd> TriMesh Element</dd>\n", " <dt>Dependencies</dt> <dd>Bokeh</dd>\n", " <dt>Backends</...

bsd-3-clause

awni/tensorflow

tensorflow/tools/docker/notebooks/2_getting_started.ipynb

1

147269

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "version": "0.3.2", "views": {}, "default_view": {}, "name": "Untitled", "provenance": [] } }, "cells": [ { "cell_type": "markdown", "metadata": { "id": "6TuWv0Y0sY8n", "colab_t...

apache-2.0

kaushik94/tardis

docs/research/code_comparison/plasma_compare/plasma_compare.ipynb

2

74046

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Plasma comparison ###" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/wkerzend/miniconda/env...

bsd-3-clause

kriukov/interval-methods

ipynb/asin-extension.ipynb

1

388777

{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Resolution of the asin() problemResolution of the asin() problem" ] }, { "cell_type": "mark...

gpl-3.0

neurodata/ndmg

tutorials/Overview.ipynb

1

22964

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ndmg Tutorial: Running Inside Python" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial provides a basic overview of how to run ndmg manually within Python. <br>\n", "We begin by check...

apache-2.0

mirjalil/DataScience

algorithms-in-C++/data-structures_00_overview.ipynb

1

769

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Data Structures in C++\n", "=======\n", "\n", "## C++ STL\n", "\n", "**Containers:** " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [],...

gpl-2.0

ahwkuepper/stdme

code/app_model_chlamydia.ipynb

1

92068

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A first simple model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import pickle\n", "import numpy ...

mit

dseuss/notebooks

Ellipsoid Method.ipynb

1

287207

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import cdd\n", "import itertools as it\n", "\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "from matplo...

unlicense

basnijholt/holoviews

examples/reference/containers/plotly/Overlay.ipynb

1

2816

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div class=\"contentcontainer med left\" style=\"margin-left: -50px;\">\n", "<dl class=\"dl-horizontal\">\n", " <dt>Title</dt> <dd>Overlay Container</dd>\n", " <dt>Dependencies</dt> <dd>Plotly</dd>\n", " <dt>Backends...

bsd-3-clause

Ccaccia73/semimonocoque

01_SemiMonoCoque.ipynb

1

215557

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Semi-Monocoque Theory" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/hom...

mit

thammegowda/notes

usc-csci-ml/hw4/src/CSCI567_hw4_fall16.ipynb

2

32928

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using Theano backend.\n" ] } ], "source": [ "from hw_utils import *\n", "from copy impo...

apache-2.0

tensorflow/neural-structured-learning

workshops/kdd_2020/adversarial_regularization_mnist.ipynb

1

45218

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "375gw63MC9Hg" }, "source": [ "##### Copyright 2020 Google LLC" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, ...

apache-2.0

elsonidoq/py-l1tf

l1tf/Quick Example.ipynb

1

167463

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Using matplotlib backend: nbAgg\n", "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "sourc...

apache-2.0

taroplus/sparkling-notebook

notebooks/Matplotlib Example.ipynb

2

18884

{ "cells" : [ { "metadata" : { "trusted" : true, "collapsed" : false, "editable" : true, "deletable" : true }, "cell_type" : "code", "source" : [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "t = np.arange(0.0, 2.0, 0.01)\n", "s = 1 + np.sin(2*np.pi*t)\n",...

apache-2.0

olafplacha/OpenCV-intro

feature matching.ipynb

1

1851179

null

mit

wcmckee/garrison-wow-track

Untitled.ipynb

1

556

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "...

mit

bgruening/galaxy-ipython

templates/notebook.ipynb

1

1083

{ "metadata": { "name": "", "signature": "sha256:%s" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Welcome to the interactive Galaxy IPython Notebook." ] }, { "cell_type...

mit

4DGenome/Chromosomal-Conformation-Course

Participants/deo/00_fastq_QC.ipynb

1

240339

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "00_fastq_QC.ipynb learning_python.ipynb\r\n" ] } ], "source": [ "!ls" ] }, { "ce...

gpl-3.0

ehsteve/ipython-notebooks

RHESSI Workshope 13 - SunPy.ipynb

2

1006964

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"https://raw.github.com/sunpy/sunpy-logo/master/generated/sunpy_logo_compact_192x239.png\">\n", "# SunPy Demo RHESSI...

bsd-3-clause

di-br/CalMAdju

doc/metric_tests/Gradients.ipynb

1

541682

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Import required modules" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "...

gpl-3.0

google/starthinker

colabs/google_api_to_bigquery.ipynb

1

9224

{ "license": "Licensed under the Apache License, Version 2.0", "copyright": "Copyright 2020 Google LLC", "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Google API To BigQuery", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernel...

apache-2.0

boya-zhou/kaggle_bimbo_reformat

notebooks/5_random_forest.ipynb

1

22221

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Random_forest_regressor\n", "# extra_tree_regressor\n", "# sklearn.svm.SVR" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { ...

mit

keiikegami/envelopetheorem

ほうらくせん.ipynb

1

181033

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# -*- coding: utf-8 -*-" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ ...

bsd-3-clause

tpin3694/tpin3694.github.io

machine-learning/f1_score.ipynb

2

3004

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: F1 Score \n", "Slug: f1_score \n", "Summary: How to evaluate a Python machine learning using F1 score. \n", "Date: 2017-09-15 12:00 \n", "Category: Machine Learning \n", "Tags: Model Evaluation\n", ...

mit

SylvainCorlay/ipywidgets

docs/source/examples/Index.ipynb

1

2451

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Back to the main [Index](../Index.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython ...

bsd-3-clause

SylvainCorlay/bqplot

examples/Index.ipynb

1

7017

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# bqplot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`bqplot` is a [Grammar of Graphics](https://www.cs.uic.edu/~wilkinson/TheGrammarOfGraphics/GOG.html) based interactive plotting framework for th...

apache-2.0

QuantStack/quantstack-talks

2020-01-09-PyData-Heidelberg/examples/gradient_descent.ipynb

1

5397

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook served by Voilà" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Notebook copied from https://github.com/ChakriCherukuri/mlviz" ] }, { "cell_type": "markdown", "metadata": {...

bsd-3-clause

savioabuga/arrows

arrows.ipynb

2

763487

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# arrows: Yet Another Twitter/Python Data Analysis\n", "## Geospatially, Temporally, and Linguistically Analyzing Tweets about Top U.S. Presidential Candidates with Pandas, TextBlob, Seaborn, and Cartopy\n", "\n", "Hi, I'm ...

mit

flaviocordova/udacity_deep_learn_project

gan_mnist/Intro_to_GANs_Solution.ipynb

1

218986

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Generative Adversarial Network\n", "\n", "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\n", "\n", ...

mit

kit-cel/wt

sigNT/tutorial/approximation.ipynb

2

46483

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Content and Objective\n", "\n", "+ Show approximations by using gaussian approximation\n", "+ Additionally, applying Gram-Schmidt for \"orthonormalizing\" a set of functions" ] }, { "cell_type": "code", "exec...

gpl-2.0

djvanhelmond/AdventofCode2015

Advent of Code - Puzzle Selector.ipynb

1

1930

{ "cells": [ { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#!/usr/bin/env python3\n", "import random" ] }, { "cell_type": "code", "execution_count": 44, "metadata": { "collapsed": false }, "outputs...

mit

kuroniko/MAT258

258a_hw4.ipynb

1

46154

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MAT 258A: Homework 4" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise 4\n", "In this exercise, I test the code of 'pure' Newton (newtmin), Newton method with Armijo backtracking (newtmi...

mit

dhuppenkothen/ShiftyLines

notebooks/ResponsesTest.ipynb

1

4248010

null

gpl-3.0

jsjol/GaussianProcessRegressionForDiffusionMRI

notebooks/show_ODFs.ipynb

1

9064

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "\n", "import os\n", "import sys\n", "module_path = os.path.abspath(os.path.join('..'))\n", "if modul...

bsd-3-clause

esa-as/2016-ml-contest

ar4/ar4_submission2_VALIDATION.ipynb

2

106501

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Facies classification using machine learning techniques\n", "Copy of <a href=\"https://home.deib.polimi.it/bestagini/\">Paolo Bestagini's</a> \"Try 2\", augmented, by Alan Richardson (Ausar Geophysical), with an ML estimator for ...

apache-2.0

nicolas998/wmf

Examples/Ejemplo_Hidrologia_Maximos.ipynb

1

595267

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Realiza el análisis hidrológico de la cuenca de Danta\n" ] }, { "cell_type": "code", "execution_count": 46, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", ...

gpl-3.0

PlanetExp/rcnn

rcnn_session.ipynb

1

24022

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "- - - - - - - - - - - - - - - - - - - - \n", "Loaded dataset data/grids_9x9_1000.hdf5\n", "X.dtyp...

apache-2.0

csunny/blog_project

source/libs/analysis/pandas/practise.ipynb

1

31442

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 7, "metadata": ...

mit

julienchastang/unidata-python-workshop

notebooks/Python_Ecosystem/Scientific_Python_Ecosystem_Overview.ipynb

1

8695

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# The Scientific Python Ecosystem\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Python\n", "\n", "Python is a interpreted, high-level programming language that is meant to be e...

mit

MaxPowerWasTaken/MaxPowerWasTaken.github.io

jupyter_notebooks/superseded/Multiprocessing in Pandas minimal.ipynb

2

1833

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Easy Multiprocessing on Pandas DataFrames" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python's `Pandas` library is great for all sorts of data-wrangling tasks. What doesn't come out of the box wi...

gpl-3.0

alex-ip/geophys_utils

examples/10_gravity_point_discovery_and_access_demo.ipynb

1

5378811

null

apache-2.0

QuantStack/quantstack-talks

2018-08-23-jupytercon-native/notebooks/xframe.ipynb

1

6900

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![xframe](../src/xframe.svg)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<center> <h1>xframe is a dataframe for C++, based on xtensor and xtl</h1> </center>" ] }, { "cell_type": "code", ...

bsd-3-clause

intel-analytics/analytics-zoo

docs/docs/colab-notebook/orca/examples/fashion_mnist_bigdl.ipynb

1

14788611

null

apache-2.0

EderSantana/blocks_contrib

tests/DelayLine.ipynb

1

85859

{ "metadata": { "name": "", "signature": "sha256:e1c45b0ff2c887a302d3c3841e0f9ac78f090f38f5e81b6c6ae1f689014d25b2" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from fuel.datasets import Dataset\n", ...

mit

imprm/nummet_I

ComputerArithmeticExamples.ipynb

1

14275

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "ESE - Numerical Methods I: Basics of Computer Arithmetic - Examples" ] }, { "cel...

mit

atulsingh0/MachineLearning

python_DC/ST_Python_02a.ipynb

1

355141

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Statistical Thinking in Python (Part 2)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# import\n", "import pandas as pd\n", ...

gpl-3.0

ibmkendrick/streamsx.health

samples/HealthcareJupyterDemo/notebooks/experimental/Create VCAP Service Credential.ipynb

3

1845

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Create `vcap_services.json` to capture Bluemix VCAP service credential\n", "\n", "Usage:\n", "* In [Bluemix](http://bluemix.net), create `Streaming Analytics` service\n", "* Open the `Streaming Analytics` service\n", ...

apache-2.0

nehal96/Deep-Learning-ND-Exercises

Sentiment Analysis/Handwritten Digit Recognition with TFLearn and MNIST/handwritten-digit-recognition-with-tflearn.ipynb

1

29680

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Handwritten Number Recognition with TFLearn and MNIST\n", "\n", "In this notebook, we'll be building a neural network that recognizes handwritten numbers 0-9. \n", "\n", "This kind of neural network is used in a varie...

mit

gregorjerse/rt2

2015_2016/lab13/Extending values on vertices.ipynb

1

6863

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Extending values on vertices to a discrete gradient vector field\n", "During extension algorithm one has to compute lover_link for every vertex in the complex. So let us implement search for the lower link first. It requires qui...

gpl-3.0

WNoxchi/Kaukasos

FADL1/importfastaitest.ipynb

1

1000

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from fastai.imports import *" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "tex...

mit

pioneers/topgear

coroutine_spin_test.ipynb

1

16679

{ "metadata": { "name": "", "signature": "sha256:246de87d1823dcfd28158f479ebeba5932bd42b37de3a5679728fa970c2fcc5c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"http://mirageforum.com/forum/at...

apache-2.0

DJCordhose/ai

notebooks/workshops/tss/cnn-standard-architectures.ipynb

2

108858

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Training on an Advanced Standard CNN Architecture\n", "* https://keras.io/applications/\n", "* The 9 Deep Learning Papers You Need To Know About: https://adeshpande3.github.io/adeshpande3.github.io/The-9-Deep-Learning-Papers-...

mit

santanche/java2learn

notebooks/pt/c05exercicios/s02resolucoes/small-challenges-03.ipynb

1

4703

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Small Challenges 3\n", "\n", "Dadas as seguintes classes:" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "public class CA {\n", " public String t...

gpl-2.0

srcole/qwm

misc/Sawtooth PAC.ipynb

1

159245

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A sawtooth wave has statistical phase-amplitude coupling\n", "\n", "This notebook is to show that a sawtooth wave will generate spurious phase-amplitude coupling, particularly at high frequencies (i.e. above 20-60Hz)\n", ...

mit

philipwangdk/HPC

uwhpsc/labs/lab17/Tridiagonal.ipynb

2

16008

{ "metadata": { "name": "", "signature": "sha256:03891f69e60e29f26299bb857e71dcb46c7d215cad80ca995116987e2a0eeb44" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solving tridiagonal systems using scipy....

mit

bajorekp/ForexPredictor

Mnist.ipynb

1

3657

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting /tmp/data/train-images...

mit

surchs/Logbooks

rescue_comp.ipynb

1

1629484

null

gpl-3.0

jakevdp/data-CDCbirths

BirthsByDay.ipynb

1

61837

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# USA Births Data\n", "\n", "*By Jake VanderPlas. See http://github.com/jakevdp/data-CDCbirths/*\n", "\n", "This dataset records birth rates in the USA by year. It was compiled from data on the [CDC website](http://www....

bsd-3-clause

darioizzo/d-CGP

doc/sphinx/notebooks/symbolic_regression_3.ipynb

1

76709

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi-objective memetic approach\n", "\n", "In this third tutorial we consider an example with two dimensional input data and we approach its solution using a multi-objective approach where, aside the loss, we consider the fo...

gpl-3.0

patrick-kidger/equinox

examples/init_apply.ipynb

1

3057

{ "cells": [ { "cell_type": "markdown", "id": "14148da3-23fb-480b-ae31-93878cda86fa", "metadata": {}, "source": [ "# Compatibility with init-apply libraries\n", "\n", "Existing JAX neural network libraries have sometimes followed the \"init/apply\" approach, in which the parameters of a netwo...

apache-2.0

aitatanit/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

Chapter3_MCMC/Ch3_IntroMCMC_PyMC3.ipynb

1

1153635

null

mit

mercybenzaquen/foundations-homework

foundations_hw/08/Homework8_benzaquen_congress_data.ipynb

2

271784

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied (use --upgrade to upgrade): pandas in /Users/mercybenzaquen/.virtualenvs/Homewor...

mit

keiikegami/theano

MLE.ipynb

2

2615

{ "cells": [ { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "theta : [ 4.05360992 1.95336957]\n", "True theta: [4, 2]\n", "sigma: 1.3160388223\n", "T...

mit

phoebe-project/phoebe2-docs

development/tutorials/datasets_advanced.ipynb

2

17640

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Advanced: Datasets\n", "============================\n", "\n", "Datasets tell PHOEBE how and at what times to compute the model. In some cases these will include the actual observational data, and in other cases may only i...

gpl-3.0

opalbert/mltutorial

notebooks/data-mining/1. Web Scraping.ipynb

4

192214

{ "metadata": { "name": "", "signature": "sha256:bda576f544c5c3c928cf09474fc234c8a15aebe832608fcd49802d53914e2869" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Tutorial Brief" ] ...

gpl-2.0

neurotechuoft/MindType

Code/V1/EEG_Channel_Exploration.ipynb

1

5526460

null

agpl-3.0

qinwf-nuan/keras-js

notebooks/layers/wrappers/TimeDistributed.ipynb

1

16195

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "from keras.models import Model\n", "f...

mit

catalystcomputing/DSIoT-Python-sessions

Session201811/code/02 Python Assignment.ipynb

1

1845

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Strings\n", "data = 'Hello World'\n", "print data[0]\n", ...

apache-2.0

tuanavu/coursera-university-of-washington

machine_learning/3_classification/assigment/week7/module-10-online-learning-assignment-graphlab.ipynb

2

443945

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Training Logistic Regression via Stochastic Gradient Ascent\n", "\n", "The goal of this notebook is to implement a logistic regression classifier using stochastic gradient ascent. You will:\n", "\n", " * Extract featu...

mit

jegibbs/phys202-2015-work

assignments/assignment11/OptimizationEx01.ipynb

1

21572

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Optimization Exercise 1" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "me...

mit

jgarciab/Stats_Python

Statistics 2014-09-29.ipynb

1

482112

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": true, "input": [ "__author__ = 'Javier Garcia-Bernardo 2014'\n", "\n", "# Math\n", "import numpy as np\n", "\n", "# Stats\...

agpl-3.0