codeparrot/github-jupyter · Datasets at Hugging Face

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fionapigott/Data-Science-45min-Intros	neural-networks-101/Neural Networks - Part 1.ipynb	6	20311	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Neural Networks - Part 1\n", "\n", "2016-06-17, Josh Montague\n", "\n", "Motivation, a little history, a naive implementation, and a discussion of neural networks.\n", "\n", "\n", "## Logistic regression\n...	unlicense
PepSalehi/tuthpc	Untitled5.ipynb	1	16328	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MPI and cluster computing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is large-scale and cluster computing?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...	bsd-3-clause
MadDataScience/DAT4	DS_Lec13-ANN.ipynb	1	500355	{ "metadata": { "celltoolbar": "Slideshow", "name": "", "signature": "sha256:bb30774b7c8076d6bcb249ec4b0009fd22aaaa8b806b858a513774e7fd9e518d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_typ...	artistic-2.0
jguhlin/nn-replicon-identification	iristest.ipynb	1	7014	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import os\n", "impor...	epl-1.0
sophie63/FlyLFM	Notebooks/Utils/.ipynb_checkpoints/100106_for_turning_components-checkpoint.ipynb	1	248238	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import stats\n", "from scipy import io\n", "import sci...	bsd-2-clause
wrobstory/sticky	examples/sticky_examples.ipynb	2	27016	{ "metadata": { "name": "", "signature": "sha256:ec3cfb790044436975aa4e3e830dafede7485be41677fcd59316a6fb326c55f9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sticky\n", "import pandas as p...	mit
ny0011/dockerspawner-edit	DataSample.ipynb	1	6640	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "이용할 column : 시도명, 자료수(도서),자료수(연속간행물),자료수(비도서)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "hypothesis : 수도권과 비수도권의 도서관당 보유한 자료수의 차이는 유의미하다." ] }, { "cell_type": "...	bsd-3-clause
JonasHarnau/apc	apc/vignettes/vignette_misspecification.ipynb	1	62574	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Misspecification Tests for Log-Normal and Over-Dispersed Poisson Chain-Ladder Models\n", "\n", "We replicate the empirical applications in [Harnau (2018)](http://mdpi.com/2227-9091/6/2/25) in Section 5.\n", "\n", "*Th...	gpl-3.0
mramire8/active	other/sent_distribution.ipynb	2	78711	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentence Distribution\n", "\n", "Sentence distribution for IMDB dataset \n", "\n", "* How many sentences p...	apache-2.0
AaronCWong/phys202-2015-work	days/day20/MoviePy.ipynb	11	702523	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Making Animations using MoviePy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows how to make animations using MoviePy and Matplotlib. Here are links to the MoviePy documentation an...	mit
anshbansal/anshbansal.github.io	udacity_data_science_notes/statistics/lesson_01/lesson_01.ipynb	1	3163	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Lesson 01 - Research Methods\n", "\n", "To believe any results of any study we need to ensure\n", "- Good sample size\n", "- Representative sample\n", "- Sound Methodology\n", "\n", ...	mit
minesh1291/Practicing-Kaggle	Sberbank2017/test_vx3_lr.ipynb	1	132755	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-05-05T21:18:23.076458Z", "start_time": "2017-05-05T21:18:23.069128Z" }, "code_folding": [], "collapsed": true }, "outputs": [], "source": [ "#version vx3\n", ...	gpl-3.0
LSSTC-DSFP/LSSTC-DSFP-Sessions	Sessions/Session05/Day5/MultiwavelengthPhotometry.ipynb	1	26194	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Matched wavelength photometry\n", "\n", "Version 0.1\n", "\n", "For today's problem, we will perform matched-aperture photometry in 3 bands on multiple galaxies within a rich galaxy cluster. Ultimately, we will be...	mit
gregoryg/cdh-projects	notebooks/jupyter/datascience/K Means Cluster Visualization.ipynb	1	7540	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizing clusters in Python\n", "\n", "I am wanting to see the results of clustering methods such as K-Means; this is my playground.\n", "\n", "Initial examples are taken from [K Means Clustering in Python](http://...	apache-2.0
evanmiltenburg/python-for-text-analysis	Assignments/ASSIGNMENT-1.ipynb	1	23090	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment 1: Calculation, Strings, Boolean Expressions and Conditions\n", "\n", "Deadline: Friday, September 9, 2021 before 3pm (submit via Canvas: Block I/Assignment 1) \n", "\n", "\n", "This assignment is *...	apache-2.0
chengsoonong/crowdastro	notebooks/107_features.ipynb	1	344544	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Features\n", "\n", "Feature distributions for each subset." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "import pickle, h5py, astropy.io.ascii as asc...	mit
danielfrg/danielfrg.github.io-source	content/blog/notebooks/2013/01/copper-machine-learning-bootstrap-bagging-python.ipynb	1	109903	{ "metadata": { "name": "Post_3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This week on my Advance Business Intelligence class we took a look at Boosting and Bagging, two concepts well known by everybo...	apache-2.0
google-research/football	gfootball/colabs/gfootball_example_from_scratch.ipynb	1	41251	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "gfootball_example_from_scratch.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "mar...	apache-2.0
phoebe-project/phoebe2-docs	2.1/tutorials/spots.ipynb	1	175826	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Binary with Spots\n", "============================\n", "\n", "Setup\n", "-----------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IMPORTANT NOTE: if using spot...	gpl-3.0
arne-cl/alt-mulig	time-series/Session 1 - Introduction to time series in Python.ipynb	1	37515	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# show all plots inside Jupyter\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, ...	gpl-3.0
rohanisaac/spectra	notebooks/find_background.ipynb	1	883223	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Intelligent background subtraction algorithm using wavelets.\n", "\n", "Also contains implementations of other wavelets and peak-finding code\n", "\n", "Runs 2017/1/9" ] }, { "cell_type": "markdown", "metad...	gpl-3.0
profxj/xastropy	xastropy/casbah/CASBAH_galaxy_database.ipynb	4	14053	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building the CASBAH Galaxy Database (v1.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SDSS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Targeting\n", ...	bsd-3-clause
gasabr/AtoD	experiments/abilities_clustering.ipynb	1	76354	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## TODO\n", "1. (One day) Write distance function which is going to use nmf classification of abilities texts.\n", "2. write a method to binarize columns by given column name\n", "3. would be nice to use some anchor abiliti...	mit
Gorgel/minkpy	analysis/notebooks/V3_analyser_dtbox.ipynb	1	335012	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "import pylab\n", "from scipy...	gpl-2.0
tensorflow/docs-l10n	site/zh-cn/tutorials/load_data/text.ipynb	1	19130	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "DweYe9FcbMK_" }, "source": [ "##### Copyright 2018 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", ...	apache-2.0
fastai/course-v3	nbs/dl1/lesson3-planet.ipynb	1	532527	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Multi-label prediction with Planet Amazon dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "%ma...	apache-2.0
masasin/spirit	notebooks/03_pre-analysis.ipynb	1	5235913	null	mit
ernestyalumni/servetheloop	packetDef/podCommands.ipynb	1	22537	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# `server/udp/podCommands.js` - from node.js /JavaScript to Python (object)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# find out w...	mit
yugangzhang/CHX_Pipelines	rheadric/XPCS_GiSAXS_Single_Run_Oct12_with_Bin_Funcs.ipynb	1	4642917	null	bsd-3-clause
taslug/esp8266-hackfest-july-2016	notebook/TasLUG July 2016.ipynb	1	1435	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TasLUG July 2016\n", "\n", "## ESP8266 Hackfest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Agenda\n", "\n", "- 6pm Arrive and Setup - order Pizza?\n", "- 6.30pm - Introduc...	gpl-3.0
CompPhysics/ComputationalPhysics2	doc/pub/week13/ipynb/week13.ipynb	1	2539	{ "cells": [ { "cell_type": "markdown", "id": "347cc549", "metadata": { "editable": true }, "source": [ "<!-- HTML file automatically generated from DocOnce source (https://github.com/doconce/doconce/)\n", "doconce format html week13.do.txt --no_mako --no_abort -->\n", "<!-- dom:TITLE:...	cc0-1.0
littleowen/Conceptor	Speaker.ipynb	2	3679167	null	gpl-3.0
mne-tools/mne-tools.github.io	0.21/_downloads/6035dcef33422511928bd2247a3d092d/plot_source_power_spectrum_opm.ipynb	1	11555	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Compute so...	bsd-3-clause
napjon/ds-nd	p1-statistics/project.ipynb	1	32267	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a Stroop task, participants are presented with a list of words, with each word displayed in a color of ink. The participant’s task is to sa...	mit
seyeunlee/AliceBob	notebooks/07_GitIntro.ipynb	1	73045	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<style>\n", "div.cell, div.text_cell_render{\n", " max-w...	apache-2.0
obulpathi/datascience	scikit/boston/boston.ipynb	1	97643	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "% matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import scipy.stats as stats\n", "import matplotlib.pyplot as plt\n", ...	apache-2.0
zlpure/CS231n	assignment1/features.ipynb	1	355838	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Image features exercise\n", "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](htt...	mit
timestocome/Test-stock-prediction-algorithms	Silver Winner Jane Street Stock Competition/graph-and-plot-feature-correlations-v2.ipynb	1	2743687	null	mit
kecnry/autofig	docs/tutorials/3d.ipynb	1	228907	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting in 3D with autofig" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import autofig\n", "import numpy as np" ] }, { "cell_type": "code", ...	gpl-3.0
sympy/scipy-2017-codegen-tutorial	index.ipynb	1	11665	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"text-align:center;\">Automatic Code Generation with SymPy</h1>\n", "\n", "<img src=\"intro-slides/sympy-notext.svg\" alt=\"sympy logo\" style=\"width:100px\">\n", "\n", "<h3 style=\"text-align:center;\">Watc...	bsd-3-clause
goddoe/CADL	session-1/session-1.ipynb	4	32631	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Session 1 - Introduction to Tensorflow\n", "<p class=\"lead\">\n", "Assignment: Creating a Dataset/Computing with Tensorflow\n", "</p>\n", "\n", "<p class=\"lead\">\n", "Parag K. Mital<br />\n", "<a href=\...	apache-2.0
dk14/machine-learning-exercises	MultiBandit.ipynb	1	1246802	null	mit
quantumlib/Cirq	docs/tutorials/shor.ipynb	1	38600	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "cedf868076a2" }, "source": [ "##### Copyright 2020 The Cirq Developers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "906e07f6e562" }, "outputs": [], "sour...	apache-2.0
amueller/scipy-2017-sklearn	notebooks/21.Unsupervised_learning-Non-linear_dimensionality_reduction.ipynb	1	8426	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "ma...	cc0-1.0
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn	doc/notebooks/expression.sum.ipynb	1	2715	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# _`expression`_`.sum(`_`exp`_`)`\n", "# _`expression`_ + _`exp`_\n", "\n", "An expression which denotes the sum (or disjunction) of both series.\n", "\n", "Preconditions:\n", "- None\n", "\n", "See also...	gpl-3.0
rajathkumarmp/BinPy	BinPy/examples/notebook/Sequential/FlipFlop/SRLatch.ipynb	5	15877	{ "metadata": { "name": "", "signature": "sha256:4819bbe8a5a2d640fb7430b44d6f07599ae9c25e7c65861336ca306e42b147ba" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Example for SRLatch" ...	bsd-3-clause
cathyq/practice-code-a-day	Search Insert Position.ipynb	1	3141	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Search Insert Position\n", "Given a sorted array and a target value, return the index if the target is found. If not, return the index where it would be if it were inserted in order. \n", "[Problem from LeetCode](https://le...	mit
spm2164/foundations-homework	06/Homework-06.ipynb	1	11608	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Submit this and your other two notebooks (Spotify + NYT) to GitHub by Monday morning. If you're struggling with the previous assignment I recommend trying this one out instead, it's a lot simpler! I'll send out some readings later in t...	artistic-2.0
henry-ngo/VIP	docs/source/tutorials/01_quickstart.ipynb	1	7865547	null	mit
logrusFr/CNAM-public	UASB03/uasb03-Choix_modele_previsonnel_catastrophes-public.ipynb	1	318034	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Projet CNAM-UASB03 - \"Prévision\" des catastrophes naturelles en France\n", "\n", "## Partie : Choix parmi les modeles conçus\n", "\n", "\n", "\n", "Dépôt du projet :\n", "\n", "* https://gitlab.com/l...	gpl-3.0
sonyahanson/assaytools	examples/ipynbs/data-analysis/grant_figures/Competition-Assay-GrantPlot.ipynb	2	98677	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Couldn't import dot_parser, loading of dot files will not be possible.\n" ] } ], "source": [ ...	lgpl-2.1
prk327/CoAca	4_Slicing_Dicing.ipynb	1	218251	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Slicing and Dicing Dataframes\n", "\n", "You have seen how to do indexing of dataframes using ```df.iloc``` and ```df.loc```. Now, let's see how to subset dataframes based on certain conditions. \n" ] }, { "cell_typ...	gpl-3.0
mne-tools/mne-tools.github.io	0.19/_downloads/9cb26d39ca23b6aac4c0d201a4775849/plot_brainstorm_data.ipynb	2	3257	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n============...	bsd-3-clause
ShubhamDebnath/Coursera-Machine-Learning	Course 5/Dinosaurus Island Character level language model final v3.ipynb	1	45021	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Character level language model - Dinosaurus land\n", "\n", "Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology res...	mit
csaladenes/blog	airports/airportia_ro_arrv_parser.ipynb	2	25631	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd, json, numpy as np\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2...	mit
keras-team/keras-io	guides/ipynb/keras_tuner/custom_tuner.ipynb	1	12122	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "# Tune hyperparameters in your custom training loop\n", "\n", "Authors: Tom O'Malley, Haifeng Jin<br>\n", "Date created: 2019/10/28<br>\n", "Last modified: 2022/01/12<br>\n", ...	apache-2.0
softwaremechanic/Miscellaneous	Julia Ex.ipynb	1	2011	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-12-13T10:27:46.487645Z", "start_time": "2017-12-13T10:27:45.440Z" } }, "outputs": [ { "data": { "text/plain": [ "randmatstat (generic function with 1 metho...	gpl-2.0
NGSchool2016/ngschool2016-materials	jupyter/ndolgikh/.ipynb_checkpoints/NGSchool_python-checkpoint.ipynb	1	104340	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Set the matplotlib magic to notebook enable inline plots" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", ...	gpl-3.0
arcturusannamalai/open-tamil	examples/keras-payil-putthagangal/linear_regression_irumarigal_ipnb.ipynb	2	45056	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "linear_regression_irumarigal.ipnb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" ...	mit
dtrimarco/blog	posts/product_data_071317.ipynb	2	10835	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas for Product Analysis Part 1: Apply and Transform\n", " \n", "Python's [pandas](http://pandas.pydata.org/) package is one of the most powerful tools for data analysis in the Python ecosystem. Built on top of NumPy, it m...	mit
abhipr1/DATA_SCIENCE_INTENSIVE	Week_1/DATA_WRANGLING/DATA_CLEANING_WITH_PANDAS/missing_data.ipynb	1	50331	{ "metadata": { "name": "", "signature": "sha256:7c0b3516450eb5c95292bb728bcec36d2a0d8595fe755ef466188af0760cfb31" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Working with missing data\...	apache-2.0
authman/DAT210x	Module5/Module5 - Lab5.ipynb	1	10496	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DAT210x - Programming with Python for DS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Module5- Lab5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "coll...	mit
chloeyangu/BigDataAnalytics	Terrorisks/Code/BT4221 - Code 2.ipynb	1	2158103	null	mit
NREL/bifacial_radiance	docs/tutorials/4 - Medium Level Example - Debugging your Scene with Custom Objects (Fixed Tilt 2-up with Torque Tube + CLEAN Routine + CustomObject).ipynb	1	30536	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4 - Medium Level Example - Debugging your Scene with Custom Objects\n", "### Fixed Tilt 2-up with Torque Tube + CLEAN Routine + CustomObject\n", "\n", "This journal has examples of various things, some which hav ebeen cov...	bsd-3-clause
thehackerwithin/berkeley	code_examples/python_mayavi/mayavi_intermediate.ipynb	1	8809	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:traits.has_traits:DEPRECATED: traits.has_traits.wrapped_class, 'the 'implements' class advisor has bee...	bsd-3-clause
alvason/diffusion-computation	stochasticD/.ipynb_checkpoints/poisson_random_distribution-checkpoint.ipynb	1	239984	{ "metadata": { "name": "", "signature": "sha256:f466673fe6909ac77ba73ab30879844b8e6a6408c71351215ed3b0162f72fa13" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Diffusion computation\n", "https:/...	gpl-2.0
CooperLuan/devops.notes	PythonScientificComputing/Job Market with Pandas Part 2.ipynb	1	16673	{ "metadata": { "name": "", "signature": "sha256:8ab6da390827d5b6b0aba7dd309d1580338e650a5f9500eba71b93ee309a5d6c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Job Market with Pandas Part 2\n", ...	mit
luwei0917/awsemmd_script	notebook/Optimization/Optimization_helperFunctions_Sep23.ipynb	1	366895	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import random\n", "import time\n", "from random import seed, randint\n", "import argparse\n", "import platform\n", "from datetime import ...	mit
berkeley-dsc/purchasing	doc/pres/bids_presentation.ipynb	2	7751920	null	isc
danielgreening/titanic-kaggle	notebooks/01-dan-titanic-notebook.ipynb	1	1487	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Titanic: Machine Learning from Disaster \| Kaggle \n", "\n", "This is a jupyter notebook exploring the [Kaggle Titanic competition](https://www.kaggle.com/c/titanic). My first proper attempt using this format so expect bad for...	mit
johnnyliu27/openmc	examples/jupyter/mgxs-part-iii.ipynb	1	73124	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This IPython Notebook illustrates the use of the `openmc.mgxs.Library` class. The `Library` class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, an...	mit
Radcliffe/project-euler	Euler 021 - Amicable numbers.ipynb	1	1766	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Euler Problem 21 - Amicable numbers\n", "\n", "Let d(n) be defined as the sum of proper divisors of n (numbers less than n which divide evenly into n).\n", "If d(a) = b and d(b) = a, where a ≠ b, then a and b are an amica...	mit
mssalvador/Fifa2018	Teknisk Tirsdag Tutorial (Supervised Learning).ipynb	1	412004	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Teknisk Tirsdag: Supervised Learning\n", "\n", "I denne opgave skal vi bruge Logistisk Regression til at forudsige hvilke danske fodboldspillere der egentlig kunne spille for en storklub." ] }, { "cell_type": "code"...	apache-2.0
fang-lei/MachineLearningCourse-Python	Exercise01.ipynb	1	469884	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Exercise 01" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q1: distribution P(x,y)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "coll...	gpl-3.0
brycepg/fto-scraper	fto_graph.ipynb	1	424400	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import OrderedDict\n", "import math\n", "import datetime\n", "import time\n", "\n", "import matplotlib.pyplot as plt\n", "import...	mit
amkatrutsa/MIPT-Opt	Spring2021/intro_gd.ipynb	1	62985	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "196b8a50-3d29-45c3-82b9-f4a09b49491d" }, "slideshow": { "slide_type": "slide" } }, "source": [ "# Введение в численные методы оптимизации (Ю. Е. Нестеров Введение в выпуклую оптимизацию, гл. 1 $\\S$ ...	mit
LedaLima/incubator-spot	spot-oa/oa/proxy/ipynb_templates/Advanced_Mode_master.ipynb	7	8193	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Apache Spot's Ipython Advanced Mode\n", "## Proxy\n", "\n", "This guide provides examples about how to request data, show data with some cool libraries like pandas and more.\n" ] }, { "cell_type": "markdown", ...	apache-2.0
debsankha/network_course_python	exercises/04-exercise-visualization.ipynb	1	3137	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "3.Graph Layouts" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) Which layouts do you think would fit best to which kind of graphs?" ] }, { "cell_type": "markdown", "metadata": {}, ...	gpl-2.0
alexwhb/algorithm-practice	jupyter-notebooks/data structures/Binary Search Tree.ipynb	1	6408	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Binary Search Trees (BST)\n", "Binary Search trees are a data structure that has many advantages over arrays and linked lists. With a binary search tree you are able to both treverse the tree fast to find nodes, and you can inser...	mit
FranciscoBraga/AprendendoPython	conexão_com_banco_sqlite/Criando um Banco SqLite.ipynb	1	6670	{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-9-a89637ef5f0d>, line 1)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-9-a89...	apache-2.0
alfkjartan/nvgimu	notebooks/.ipynb_checkpoints/Get started-checkpoint.ipynb	1	182290	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started with the analysis of nvg data\n", "This notebook assumes that data exists in a database in the hdf5 format. For instructions how to set up the database with data see [../readme.md].\n", "\n", "## Import mo...	gpl-3.0
egovernments/analytics	jupyter-notebooks/EDA/EDANotebook2.ipynb	2	22400	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "library(dplyr)\n", "library(ggplot2)\n", "library(plotly)\n", "a = read.csv(\"/home/mansiarora/Documents/DataCor_egov/Data/ddive_rolled_seq.csv\")\n", ...	mit
fionapigott/Data-Science-45min-Intros	count-min-101/CountMinSketch.ipynb	14	14934	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Idea of Count Min sketch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We map the input value to _multiple_ points in a _relatively small_ output space. Therefore, the count associated with a...	unlicense
tensorflow/docs-l10n	site/zh-cn/lattice/tutorials/shape_constraints_for_ethics.ipynb	1	39559	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "R2AxpObRncMd" }, "source": [ "*Copyright 2020 The TensorFlow Authors.*" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...	apache-2.0
sauloal/ipython	probes/probes_cfg_images.ipynb	1	311910	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parameters" ] }, { "cell_type": "code", "execution_count": 391, "metadata": { "collapsed": true }, "outputs": [], "source": [ "CONFIG_LOCAL = False\n", "#CONFIG_LOCAL = True" ] }, { "cel...	mit
Jackie789/JupyterNotebooks	CorrectingForAssumptions.ipynb	1	87042	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Engineering Existing Data to Follow Multivariate Linear Regression Assumptions\n", "\n", "Jackie Zuker\n", "\n", "### Assumptions: \n", "1. Linear relationship - Features should have a linear relationship wit...	gpl-3.0
pycroscopy/pycroscopy	jupyter_notebooks/image_registration.ipynb	1	25183	{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": false, "hideOutput": true, "hidePrompt": false }, "source": [ "<font size = \"5\"> [Image Registration](image_registration.ipynb) </font>\n", "\n", "<hr style=\"height:1px;border-top:4px solid #FF8200\" />\n", ...	mit
surchs/Logbooks	cluster_pheno_demo_10_24_14.ipynb	1	3639519	null	gpl-3.0
ORNL-CEES/Cap	python/example/EffectsOfInhomogeneitiesOnEIS.ipynb	3	2923	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Studying the effect of pore size distribution on impedance spectroscopy" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "sou...	bsd-3-clause
gfabieno/SeisCL	docs/notebooks/Accuracy/AnalyticalSolutions.ipynb	1	614	{ "cells": [ { "cell_type": "markdown", "id": "royal-facility", "metadata": {}, "source": [ "# Analytical Solutions\n", "\n", "Under Development ..." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_inf...	gpl-3.0
dirmeier/dataframe	examples/demo.ipynb	1	4121	{ "cells": [ { "cell_type": "code", "source": [ "from sklearn import datasets\n", "import re\n", "from dataframe import Callable\n", "import numpy" ], "outputs": [], "execution_count": 1, "metadata": { "collapsed": false, "outputH...	gpl-3.0
ImAlexisSaez/deep-learning-specialization-coursera	course_1/week_3/assignment_1/planar_data_classification_with_one_hidden_layer_v1.ipynb	1	557787	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Planar data classification with one hidden layer\n", "\n", "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between th...	mit
dtamayo/rebound	ipython_examples/HyperbolicOrbits.ipynb	2	68225	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading Hyperbolic Orbits into REBOUND\n", "\n", "Imagine we have a table of orbital elements for comets (kindly provided by Toni Engelhardt)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { ...	gpl-3.0
wathen/PhD	MHD/FEniCS/ShiftCurlCurl/CppGradient/Efficient/.ipynb_checkpoints/Untitled4-checkpoint.ipynb	1	10513	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from dolfin import *\n", "import numpy\n", "nn = 1\n", "mesh = UnitCubeMesh(int(nn),int(nn),int(nn))\n", "ord...	mit
getsmarter/bda	module_4/M4_NB3_NetworkClustering.ipynb	1	24094	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">Python 3.6 Jupyter Notebook</div>\n", "\n", "# Finding connected components using clustering\n", "\n", "<br><div class=\"alert alert-warning\">\n", "<b>Note that this notebook contains advanced...	mit
mjbommar/cscs-530-w2016	samples/cscs530-w2015-midterm-sample1.ipynb	1	19277	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Midterm \n", "\n", "### Goal\n", "\n", "I will explore whether a network-theory driven approach shown to improve the efficiency of an agricultural extension program is sensitive to the models and parameters originally...	bsd-2-clause
ppyht2/tf-exercise	012. Unsupervised MNIST/Unsupervised MNIST.ipynb	1	59721	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Unsupervised Learning on the MNIST dataset " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable"...	gpl-3.0
OceanPARCELS/parcels	parcels/examples/tutorial_sampling.ipynb	1	252044	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Field sampling tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The particle trajectories allow us to study fields like temperature, plastic concentration or chlorophyll from a Lagrangian per...	mit
rishuatgithub/MLPy	torch/10. JPEG - CNN + AlexNet.ipynb	1	1910035	null	apache-2.0
Luke035/dlnd-lessons	gan_mnist/Intro_to_GANs_Solution.ipynb	1	209536	{ "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

fionapigott/Data-Science-45min-Intros

neural-networks-101/Neural Networks - Part 1.ipynb

6

20311

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Neural Networks - Part 1\n", "\n", "2016-06-17, Josh Montague\n", "\n", "Motivation, a little history, a naive implementation, and a discussion of neural networks.\n", "\n", "\n", "## Logistic regression\n...

unlicense

PepSalehi/tuthpc

Untitled5.ipynb

1

16328

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MPI and cluster computing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What is large-scale and cluster computing?" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...

bsd-3-clause

MadDataScience/DAT4

DS_Lec13-ANN.ipynb

1

500355

{ "metadata": { "celltoolbar": "Slideshow", "name": "", "signature": "sha256:bb30774b7c8076d6bcb249ec4b0009fd22aaaa8b806b858a513774e7fd9e518d" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_typ...

artistic-2.0

jguhlin/nn-replicon-identification

iristest.ipynb

1

7014

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import absolute_import\n", "from __future__ import division\n", "from __future__ import print_function\n", "\n", "import os\n", "impor...

epl-1.0

sophie63/FlyLFM

Notebooks/Utils/.ipynb_checkpoints/100106_for_turning_components-checkpoint.ipynb

1

248238

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy import stats\n", "from scipy import io\n", "import sci...

bsd-2-clause

wrobstory/sticky

examples/sticky_examples.ipynb

2

27016

{ "metadata": { "name": "", "signature": "sha256:ec3cfb790044436975aa4e3e830dafede7485be41677fcd59316a6fb326c55f9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sticky\n", "import pandas as p...

mit

ny0011/dockerspawner-edit

DataSample.ipynb

1

6640

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "이용할 column : 시도명, 자료수(도서),자료수(연속간행물),자료수(비도서)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "hypothesis : 수도권과 비수도권의 도서관당 보유한 자료수의 차이는 유의미하다." ] }, { "cell_type": "...

bsd-3-clause

JonasHarnau/apc

apc/vignettes/vignette_misspecification.ipynb

1

62574

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Misspecification Tests for Log-Normal and Over-Dispersed Poisson Chain-Ladder Models\n", "\n", "We replicate the empirical applications in [Harnau (2018)](http://mdpi.com/2227-9091/6/2/25) in Section 5.\n", "\n", "*Th...

gpl-3.0

mramire8/active

other/sent_distribution.ipynb

2

78711

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentence Distribution\n", "\n", "Sentence distribution for IMDB dataset \n", "\n", "* How many sentences p...

apache-2.0

AaronCWong/phys202-2015-work

days/day20/MoviePy.ipynb

11

702523

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Making Animations using MoviePy" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows how to make animations using MoviePy and Matplotlib. Here are links to the MoviePy documentation an...

mit

anshbansal/anshbansal.github.io

udacity_data_science_notes/statistics/lesson_01/lesson_01.ipynb

1

3163

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Lesson 01 - Research Methods\n", "\n", "To believe any results of any study we need to ensure\n", "- Good sample size\n", "- Representative sample\n", "- Sound Methodology\n", "\n", ...

mit

minesh1291/Practicing-Kaggle

Sberbank2017/test_vx3_lr.ipynb

1

132755

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-05-05T21:18:23.076458Z", "start_time": "2017-05-05T21:18:23.069128Z" }, "code_folding": [], "collapsed": true }, "outputs": [], "source": [ "#version vx3\n", ...

gpl-3.0

LSSTC-DSFP/LSSTC-DSFP-Sessions

Sessions/Session05/Day5/MultiwavelengthPhotometry.ipynb

1

26194

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Matched wavelength photometry\n", "\n", "**Version 0.1**\n", "\n", "For today's problem, we will perform matched-aperture photometry in 3 bands on multiple galaxies within a rich galaxy cluster. Ultimately, we will be...

mit

gregoryg/cdh-projects

notebooks/jupyter/datascience/K Means Cluster Visualization.ipynb

1

7540

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Visualizing clusters in Python\n", "\n", "I am wanting to see the results of clustering methods such as K-Means; this is my playground.\n", "\n", "Initial examples are taken from [K Means Clustering in Python](http://...

apache-2.0

evanmiltenburg/python-for-text-analysis

Assignments/ASSIGNMENT-1.ipynb

1

23090

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Assignment 1: Calculation, Strings, Boolean Expressions and Conditions\n", "\n", "**Deadline: Friday, September 9, 2021 before 3pm (submit via Canvas: Block I/Assignment 1)** \n", "\n", "\n", "This assignment is *...

apache-2.0

chengsoonong/crowdastro

notebooks/107_features.ipynb

1

344544

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Features\n", "\n", "Feature distributions for each subset." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "import pickle, h5py, astropy.io.ascii as asc...

mit

danielfrg/danielfrg.github.io-source

content/blog/notebooks/2013/01/copper-machine-learning-bootstrap-bagging-python.ipynb

1

109903

{ "metadata": { "name": "Post_3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This week on my Advance Business Intelligence class we took a look at Boosting and Bagging, two concepts well known by everybo...

apache-2.0

google-research/football

gfootball/colabs/gfootball_example_from_scratch.ipynb

1

41251

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "gfootball_example_from_scratch.ipynb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" } }, "cells": [ { "cell_type": "mar...

apache-2.0

phoebe-project/phoebe2-docs

2.1/tutorials/spots.ipynb

1

175826

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Binary with Spots\n", "============================\n", "\n", "Setup\n", "-----------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**IMPORTANT NOTE:** if using spot...

gpl-3.0

arne-cl/alt-mulig

time-series/Session 1 - Introduction to time series in Python.ipynb

1

37515

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# show all plots inside Jupyter\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, ...

gpl-3.0

rohanisaac/spectra

notebooks/find_background.ipynb

1

883223

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Intelligent background subtraction algorithm using wavelets.\n", "\n", "Also contains implementations of other wavelets and peak-finding code\n", "\n", "Runs 2017/1/9" ] }, { "cell_type": "markdown", "metad...

gpl-3.0

profxj/xastropy

xastropy/casbah/CASBAH_galaxy_database.ipynb

4

14053

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building the CASBAH Galaxy Database (v1.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## SDSS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Targeting\n", ...

bsd-3-clause

gasabr/AtoD

experiments/abilities_clustering.ipynb

1

76354

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## TODO\n", "1. (One day) Write distance function which is going to use nmf classification of abilities texts.\n", "2. write a method to binarize columns by given column name\n", "3. would be nice to use some anchor abiliti...

mit

Gorgel/minkpy

analysis/notebooks/V3_analyser_dtbox.ipynb

1

335012

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "import pylab\n", "from scipy...

gpl-2.0

tensorflow/docs-l10n

site/zh-cn/tutorials/load_data/text.ipynb

1

19130

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "DweYe9FcbMK_" }, "source": [ "##### Copyright 2018 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", ...

apache-2.0

fastai/course-v3

nbs/dl1/lesson3-planet.ipynb

1

532527

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Multi-label prediction with Planet Amazon dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%reload_ext autoreload\n", "%autoreload 2\n", "%ma...

apache-2.0

masasin/spirit

notebooks/03_pre-analysis.ipynb

1

5235913

null

mit

ernestyalumni/servetheloop

packetDef/podCommands.ipynb

1

22537

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# `server/udp/podCommands.js` - from node.js /JavaScript to Python (object)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# find out w...

mit

yugangzhang/CHX_Pipelines

rheadric/XPCS_GiSAXS_Single_Run_Oct12_with_Bin_Funcs.ipynb

1

4642917

null

bsd-3-clause

taslug/esp8266-hackfest-july-2016

notebook/TasLUG July 2016.ipynb

1

1435

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TasLUG July 2016\n", "\n", "## ESP8266 Hackfest" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Agenda\n", "\n", "- 6pm Arrive and Setup - order Pizza?\n", "- 6.30pm - Introduc...

gpl-3.0

CompPhysics/ComputationalPhysics2

doc/pub/week13/ipynb/week13.ipynb

1

2539

{ "cells": [ { "cell_type": "markdown", "id": "347cc549", "metadata": { "editable": true }, "source": [ "\n", "<!-- dom:TITLE:...

cc0-1.0

littleowen/Conceptor

Speaker.ipynb

2

3679167

null

gpl-3.0

mne-tools/mne-tools.github.io

0.21/_downloads/6035dcef33422511928bd2247a3d092d/plot_source_power_spectrum_opm.ipynb

1

11555

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

bsd-3-clause

napjon/ds-nd

p1-statistics/project.ipynb

1

32267

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a Stroop task, participants are presented with a list of words, with each word displayed in a color of ink. The participant’s task is to sa...

mit

seyeunlee/AliceBob

notebooks/07_GitIntro.ipynb

1

73045

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "<style>\n", "div.cell, div.text_cell_render{\n", " max-w...

apache-2.0

obulpathi/datascience

scikit/boston/boston.ipynb

1

97643

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

apache-2.0

zlpure/CS231n

assignment1/features.ipynb

1

355838

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Image features exercise\n", "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [assignments page](htt...

mit

timestocome/Test-stock-prediction-algorithms

Silver Winner Jane Street Stock Competition/graph-and-plot-feature-correlations-v2.ipynb

1

2743687

null

mit

kecnry/autofig

docs/tutorials/3d.ipynb

1

228907

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting in 3D with autofig" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import autofig\n", "import numpy as np" ] }, { "cell_type": "code", ...

gpl-3.0

sympy/scipy-2017-codegen-tutorial

index.ipynb

1

11665

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 style=\"text-align:center;\">Automatic Code Generation with SymPy</h1>\n", "\n", "<img src=\"intro-slides/sympy-notext.svg\" alt=\"sympy logo\" style=\"width:100px\">\n", "\n", "<h3 style=\"text-align:center;\">Watc...

bsd-3-clause

goddoe/CADL

session-1/session-1.ipynb

4

32631

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Session 1 - Introduction to Tensorflow\n", "<p class=\"lead\">\n", "Assignment: Creating a Dataset/Computing with Tensorflow\n", "</p>\n", "\n", "<p class=\"lead\">\n", "Parag K. Mital<br />\n", "<a href=\...

apache-2.0

dk14/machine-learning-exercises

MultiBandit.ipynb

1

1246802

null

mit

quantumlib/Cirq

docs/tutorials/shor.ipynb

1

38600

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "cedf868076a2" }, "source": [ "##### Copyright 2020 The Cirq Developers" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "id": "906e07f6e562" }, "outputs": [], "sour...

apache-2.0

amueller/scipy-2017-sklearn

notebooks/21.Unsupervised_learning-Non-linear_dimensionality_reduction.ipynb

1

8426

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "ma...

cc0-1.0

pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn

doc/notebooks/expression.sum.ipynb

1

2715

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# _`expression`_`.sum(`_`exp`_`)`\n", "# _`expression`_ + _`exp`_\n", "\n", "An expression which denotes the sum (or disjunction) of both series.\n", "\n", "Preconditions:\n", "- None\n", "\n", "See also...

gpl-3.0

rajathkumarmp/BinPy

BinPy/examples/notebook/Sequential/FlipFlop/SRLatch.ipynb

5

15877

{ "metadata": { "name": "", "signature": "sha256:4819bbe8a5a2d640fb7430b44d6f07599ae9c25e7c65861336ca306e42b147ba" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Example for SRLatch" ...

bsd-3-clause

cathyq/practice-code-a-day

Search Insert Position.ipynb

1

3141

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Search Insert Position\n", "Given a sorted array and a target value, return the index if the target is found. If not, return the index where it would be if it were inserted in order. \n", "[Problem from LeetCode](https://le...

mit

spm2164/foundations-homework

06/Homework-06.ipynb

1

11608

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Submit this and your other two notebooks (Spotify + NYT) to GitHub by Monday morning. If you're struggling with the previous assignment I recommend trying this one out instead, it's a lot simpler! I'll send out some readings later in t...

artistic-2.0

henry-ngo/VIP

docs/source/tutorials/01_quickstart.ipynb

1

7865547

null

mit

logrusFr/CNAM-public

UASB03/uasb03-Choix_modele_previsonnel_catastrophes-public.ipynb

1

318034

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Projet CNAM-UASB03 - \"Prévision\" des catastrophes naturelles en France\n", "\n", "## Partie : Choix parmi les modeles conçus\n", "\n", "\n", "\n", "Dépôt du projet :\n", "\n", "* https://gitlab.com/l...

gpl-3.0

sonyahanson/assaytools

examples/ipynbs/data-analysis/grant_figures/Competition-Assay-GrantPlot.ipynb

2

98677

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Couldn't import dot_parser, loading of dot files will not be possible.\n" ] } ], "source": [ ...

lgpl-2.1

prk327/CoAca

4_Slicing_Dicing.ipynb

1

218251

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Slicing and Dicing Dataframes\n", "\n", "You have seen how to do indexing of dataframes using ```df.iloc``` and ```df.loc```. Now, let's see how to subset dataframes based on certain conditions. \n" ] }, { "cell_typ...

gpl-3.0

mne-tools/mne-tools.github.io

0.19/_downloads/9cb26d39ca23b6aac4c0d201a4775849/plot_brainstorm_data.ipynb

2

3257

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

bsd-3-clause

ShubhamDebnath/Coursera-Machine-Learning

Course 5/Dinosaurus Island Character level language model final v3.ipynb

1

45021

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Character level language model - Dinosaurus land\n", "\n", "Welcome to Dinosaurus Island! 65 million years ago, dinosaurs existed, and in this assignment they are back. You are in charge of a special task. Leading biology res...

mit

csaladenes/blog

airports/airportia_ro_arrv_parser.ipynb

2

25631

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

mit

keras-team/keras-io

guides/ipynb/keras_tuner/custom_tuner.ipynb

1

12122

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text" }, "source": [ "# Tune hyperparameters in your custom training loop\n", "\n", "**Authors:** Tom O'Malley, Haifeng Jin<br>\n", "**Date created:** 2019/10/28<br>\n", "**Last modified:** 2022/01/12<br>\n", ...

apache-2.0

softwaremechanic/Miscellaneous

Julia Ex.ipynb

1

2011

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-12-13T10:27:46.487645Z", "start_time": "2017-12-13T10:27:45.440Z" } }, "outputs": [ { "data": { "text/plain": [ "randmatstat (generic function with 1 metho...

gpl-2.0

NGSchool2016/ngschool2016-materials

jupyter/ndolgikh/.ipynb_checkpoints/NGSchool_python-checkpoint.ipynb

1

104340

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Set the matplotlib magic to notebook enable inline plots" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", ...

gpl-3.0

arcturusannamalai/open-tamil

examples/keras-payil-putthagangal/linear_regression_irumarigal_ipnb.ipynb

2

45056

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "linear_regression_irumarigal.ipnb", "provenance": [], "collapsed_sections": [] }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, "language_info": { "name": "python" ...

mit

dtrimarco/blog

posts/product_data_071317.ipynb

2

10835

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Pandas for Product Analysis Part 1: Apply and Transform\n", " \n", "Python's [pandas](http://pandas.pydata.org/) package is one of the most powerful tools for data analysis in the Python ecosystem. Built on top of NumPy, it m...

mit

abhipr1/DATA_SCIENCE_INTENSIVE

Week_1/DATA_WRANGLING/DATA_CLEANING_WITH_PANDAS/missing_data.ipynb

1

50331

{ "metadata": { "name": "", "signature": "sha256:7c0b3516450eb5c95292bb728bcec36d2a0d8595fe755ef466188af0760cfb31" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Working with missing data\...

apache-2.0

authman/DAT210x

Module5/Module5 - Lab5.ipynb

1

10496

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DAT210x - Programming with Python for DS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Module5- Lab5" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "coll...

mit

chloeyangu/BigDataAnalytics

Terrorisks/Code/BT4221 - Code 2.ipynb

1

2158103

null

mit

NREL/bifacial_radiance

docs/tutorials/4 - Medium Level Example - Debugging your Scene with Custom Objects (Fixed Tilt 2-up with Torque Tube + CLEAN Routine + CustomObject).ipynb

1

30536

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4 - Medium Level Example - Debugging your Scene with Custom Objects\n", "### Fixed Tilt 2-up with Torque Tube + CLEAN Routine + CustomObject\n", "\n", "This journal has examples of various things, some which hav ebeen cov...

bsd-3-clause

thehackerwithin/berkeley

code_examples/python_mayavi/mayavi_intermediate.ipynb

1

8809

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING:traits.has_traits:DEPRECATED: traits.has_traits.wrapped_class, 'the 'implements' class advisor has bee...

bsd-3-clause

alvason/diffusion-computation

stochasticD/.ipynb_checkpoints/poisson_random_distribution-checkpoint.ipynb

1

239984

{ "metadata": { "name": "", "signature": "sha256:f466673fe6909ac77ba73ab30879844b8e6a6408c71351215ed3b0162f72fa13" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Diffusion computation\n", "https:/...

gpl-2.0

CooperLuan/devops.notes

PythonScientificComputing/Job Market with Pandas Part 2.ipynb

1

16673

{ "metadata": { "name": "", "signature": "sha256:8ab6da390827d5b6b0aba7dd309d1580338e650a5f9500eba71b93ee309a5d6c" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Job Market with Pandas Part 2\n", ...

mit

luwei0917/awsemmd_script

notebook/Optimization/Optimization_helperFunctions_Sep23.ipynb

1

366895

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import random\n", "import time\n", "from random import seed, randint\n", "import argparse\n", "import platform\n", "from datetime import ...

mit

berkeley-dsc/purchasing

doc/pres/bids_presentation.ipynb

2

7751920

null

isc

danielgreening/titanic-kaggle

notebooks/01-dan-titanic-notebook.ipynb

1

1487

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Titanic: Machine Learning from Disaster | Kaggle \n", "\n", "This is a jupyter notebook exploring the [Kaggle Titanic competition](https://www.kaggle.com/c/titanic). My first proper attempt using this format so expect bad for...

mit

johnnyliu27/openmc

examples/jupyter/mgxs-part-iii.ipynb

1

73124

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This IPython Notebook illustrates the use of the **`openmc.mgxs.Library`** class. The `Library` class is designed to automate the calculation of multi-group cross sections for use cases with one or more domains, cross section types, an...

mit

Radcliffe/project-euler

Euler 021 - Amicable numbers.ipynb

1

1766

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Euler Problem 21 - Amicable numbers\n", "\n", "Let d(n) be defined as the sum of proper divisors of n (numbers less than n which divide evenly into n).\n", "If d(a) = b and d(b) = a, where a ≠ b, then a and b are an amica...

mit

mssalvador/Fifa2018

Teknisk Tirsdag Tutorial (Supervised Learning).ipynb

1

412004

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Teknisk Tirsdag: Supervised Learning\n", "\n", "I denne opgave skal vi bruge Logistisk Regression til at forudsige hvilke danske fodboldspillere der egentlig kunne spille for en storklub." ] }, { "cell_type": "code"...

apache-2.0

fang-lei/MachineLearningCourse-Python

Exercise01.ipynb

1

469884

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "Exercise 01" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Q1: distribution P(x,y)" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "coll...

gpl-3.0

brycepg/fto-scraper

fto_graph.ipynb

1

424400

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import OrderedDict\n", "import math\n", "import datetime\n", "import time\n", "\n", "import matplotlib.pyplot as plt\n", "import...

mit

amkatrutsa/MIPT-Opt

Spring2021/intro_gd.ipynb

1

62985

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbpresent": { "id": "196b8a50-3d29-45c3-82b9-f4a09b49491d" }, "slideshow": { "slide_type": "slide" } }, "source": [ "# Введение в численные методы оптимизации (Ю. Е. Нестеров Введение в выпуклую оптимизацию, гл. 1 $\\S$ ...

mit

LedaLima/incubator-spot

spot-oa/oa/proxy/ipynb_templates/Advanced_Mode_master.ipynb

7

8193

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Apache Spot's Ipython Advanced Mode\n", "## Proxy\n", "\n", "This guide provides examples about how to request data, show data with some cool libraries like pandas and more.\n" ] }, { "cell_type": "markdown", ...

apache-2.0

debsankha/network_course_python

exercises/04-exercise-visualization.ipynb

1

3137

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**3.Graph Layouts**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "a) Which layouts do you think would fit best to which kind of graphs?" ] }, { "cell_type": "markdown", "metadata": {}, ...

gpl-2.0

alexwhb/algorithm-practice

jupyter-notebooks/data structures/Binary Search Tree.ipynb

1

6408

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Binary Search Trees (BST)\n", "Binary Search trees are a data structure that has many advantages over arrays and linked lists. With a binary search tree you are able to both treverse the tree fast to find nodes, and you can inser...

mit

FranciscoBraga/AprendendoPython

conexão_com_banco_sqlite/Criando um Banco SqLite.ipynb

1

6670

{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "ename": "SyntaxError", "evalue": "invalid syntax (<ipython-input-9-a89637ef5f0d>, line 1)", "output_type": "error", "traceback": [ "\u001b[1;36m File \u001b[1;32m\"<ipython-input-9-a89...

apache-2.0

alfkjartan/nvgimu

notebooks/.ipynb_checkpoints/Get started-checkpoint.ipynb

1

182290

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting started with the analysis of nvg data\n", "This notebook assumes that data exists in a database in the hdf5 format. For instructions how to set up the database with data see [../readme.md].\n", "\n", "## Import mo...

gpl-3.0

egovernments/analytics

jupyter-notebooks/EDA/EDANotebook2.ipynb

2

22400

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "library(dplyr)\n", "library(ggplot2)\n", "library(plotly)\n", "a = read.csv(\"/home/mansiarora/Documents/DataCor_egov/Data/ddive_rolled_seq.csv\")\n", ...

mit

fionapigott/Data-Science-45min-Intros

count-min-101/CountMinSketch.ipynb

14

14934

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic Idea of Count Min sketch" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We map the input value to _multiple_ points in a _relatively small_ output space. Therefore, the count associated with a...

unlicense

tensorflow/docs-l10n

site/zh-cn/lattice/tutorials/shape_constraints_for_ethics.ipynb

1

39559

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "R2AxpObRncMd" }, "source": [ "***Copyright 2020 The TensorFlow Authors.***" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...

apache-2.0

sauloal/ipython

probes/probes_cfg_images.ipynb

1

311910

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parameters" ] }, { "cell_type": "code", "execution_count": 391, "metadata": { "collapsed": true }, "outputs": [], "source": [ "CONFIG_LOCAL = False\n", "#CONFIG_LOCAL = True" ] }, { "cel...

mit

Jackie789/JupyterNotebooks

CorrectingForAssumptions.ipynb

1

87042

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Engineering Existing Data to Follow Multivariate Linear Regression Assumptions\n", "\n", "Jackie Zuker\n", "\n", "### Assumptions: \n", "1. **Linear relationship** - Features should have a linear relationship wit...

gpl-3.0

pycroscopy/pycroscopy

jupyter_notebooks/image_registration.ipynb

1

25183

{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": false, "hideOutput": true, "hidePrompt": false }, "source": [ "<font size = \"5\"> **[Image Registration](image_registration.ipynb)** </font>\n", "\n", "<hr style=\"height:1px;border-top:4px solid #FF8200\" />\n", ...

mit

surchs/Logbooks

cluster_pheno_demo_10_24_14.ipynb

1

3639519

null

gpl-3.0

ORNL-CEES/Cap

python/example/EffectsOfInhomogeneitiesOnEIS.ipynb

3

2923

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Studying the effect of pore size distribution on impedance spectroscopy" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": false }, "outputs": [], "sou...

bsd-3-clause

gfabieno/SeisCL

docs/notebooks/Accuracy/AnalyticalSolutions.ipynb

1

614

{ "cells": [ { "cell_type": "markdown", "id": "royal-facility", "metadata": {}, "source": [ "# Analytical Solutions\n", "\n", "Under Development ..." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_inf...

gpl-3.0

dirmeier/dataframe

examples/demo.ipynb

1

4121

{ "cells": [ { "cell_type": "code", "source": [ "from sklearn import datasets\n", "import re\n", "from dataframe import Callable\n", "import numpy" ], "outputs": [], "execution_count": 1, "metadata": { "collapsed": false, "outputH...

gpl-3.0

ImAlexisSaez/deep-learning-specialization-coursera

course_1/week_3/assignment_1/planar_data_classification_with_one_hidden_layer_v1.ipynb

1

557787

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Planar data classification with one hidden layer\n", "\n", "Welcome to your week 3 programming assignment. It's time to build your first neural network, which will have a hidden layer. You will see a big difference between th...

mit

dtamayo/rebound

ipython_examples/HyperbolicOrbits.ipynb

2

68225

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading Hyperbolic Orbits into REBOUND\n", "\n", "Imagine we have a table of orbital elements for comets (kindly provided by Toni Engelhardt)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { ...

gpl-3.0

wathen/PhD

MHD/FEniCS/ShiftCurlCurl/CppGradient/Efficient/.ipynb_checkpoints/Untitled4-checkpoint.ipynb

1

10513

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from dolfin import *\n", "import numpy\n", "nn = 1\n", "mesh = UnitCubeMesh(int(nn),int(nn),int(nn))\n", "ord...

mit

getsmarter/bda

module_4/M4_NB3_NetworkClustering.ipynb

1

24094

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<div align=\"right\">Python 3.6 Jupyter Notebook</div>\n", "\n", "# Finding connected components using clustering\n", "\n", "<br><div class=\"alert alert-warning\">\n", "<b>Note that this notebook contains advanced...

mit

mjbommar/cscs-530-w2016

samples/cscs530-w2015-midterm-sample1.ipynb

1

19277

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Midterm \n", "\n", "### Goal\n", "\n", "I will explore whether a network-theory driven approach shown to improve the efficiency of an agricultural extension program is sensitive to the models and parameters originally...

bsd-2-clause

ppyht2/tf-exercise

012. Unsupervised MNIST/Unsupervised MNIST.ipynb

1

59721

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Unsupervised Learning on the MNIST dataset " ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": true, "deletable": true, "editable"...

gpl-3.0

OceanPARCELS/parcels

parcels/examples/tutorial_sampling.ipynb

1

252044

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Field sampling tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The particle trajectories allow us to study fields like temperature, plastic concentration or chlorophyll from a Lagrangian per...

mit

rishuatgithub/MLPy

torch/10. JPEG - CNN + AlexNet.ipynb

1

1910035

null

apache-2.0

Luke035/dlnd-lessons

gan_mnist/Intro_to_GANs_Solution.ipynb

1

209536

{ "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