codeparrot/github-jupyter · Datasets at Hugging Face

repo_name stringlengths 6 92	path stringlengths 7 220	copies stringclasses 78 values	size stringlengths 2 9	content stringlengths 15 1.05M ⌀	license stringclasses 15 values
SyrakuShaikh/python	learning/scientific_computation/Cython/cb_RNG1.ipynb	1	2602	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-06-15T01:49:43.054052Z", "start_time": "2017-06-15T01:49:42.727026Z" }, "collapsed": true }, "outputs": [], "source": [ "%load_ext Cython" ] }, { "cell_type...	gpl-3.0
ericmjl/Network-Analysis-Made-Simple	archive/1-introduction.ipynb	1	8283	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# Before We Start!\n", "\n", "1. Look at the instructions on the whiteboard.\n", "1. Github repository for these notebooks: **github.com/ericmjl/Network-Analysis-Made-S...	mit
M-R-Houghton/euroscipy_2015	matplotlib/mpl_tutorial.ipynb	1	496538	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EuroSciPy 2015 - matplotlib tutorial" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Simple plot" ] }, { "cell_type": "markdown", "metadata": {}, "source...	mit
PythonFreeCourse/Notebooks	week07/1_Classes.ipynb	1	85170	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/logo.jpg\" style=\"display: block; margin-left: auto; margin-right: auto;\" alt=\"לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיז...	mit
taku-y/bmlingam	doc/notebook/tests/test_cli.ipynb	2	18432	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "IPython.notebook.set_autosave_interval(0)" ] }, "metadata": {}, "output_type": "display_data" }, { ...	mit
jamesmarva/maths-with-python	04-basic-plotting.ipynb	3	83290	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many Python plotting libraries depending on your purpose. However, the standard general-purpose library is `matplotlib`. This is oft...	mit
ellisonbg/talk-2015	12-JupyterLab.ipynb	1	890612	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building Blocks for Interactive Computing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What are the building blocks for interactive computing?" ] }, { "cell_type": "code", "execution_...	mit
NelisW/ComputationalRadiometry	07-Optical-Sources.ipynb	1	1167644	null	mpl-2.0
nick-youngblut/SIPSim	ipynb/bac_genome/n1147/microBetaDiv_valueRanges.ipynb	1	16797	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Goal\n", "\n", "* Get values ranges for microBetaDiv simulation run\n", " * values for MS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting paths" ] }, { "cell_type": "...	mit
IST256/learn-python	content/lessons/08-Lists/ETEE-Bad-Password-Checker.ipynb	1	2962	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# End-To-End Example: Bad Password Checker\n", "\n", "- Read in list of bad passwords from file `bad-passwords.txt`\n", "- Main program loop which:\n", " - inputs a password \n", " - checks whether the password i...	mit
tensorflow/docs-l10n	site/ko/tutorials/estimator/premade.ipynb	1	21019	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "1Z6Wtb_jisbA" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...	apache-2.0
HSE-LaMBDA/modern-technologies-for-ml-and-big-data	lecture9/Word2Vec.ipynb	1	5853	{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "println(sc.version)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "va...	mit
SiggyF/notebooks	esmf regrid.ipynb	1	6029	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "os.environ['PATH'] = os.environ['PATH'] + ':' + '/home/fedor/Checkouts/esmf/DEFAULTINSTALLDIR/bin/binO/Linux.gfo...	gpl-3.0
celiacintas/dss_practica	ipynb/fit & predict.ipynb	1	5788145	null	gpl-2.0
jnw29/AvrPto_Transcriptome	List_generator_for_genes_meeting_specific_cutoffs_vs_Mock.ipynb	1	12226	{ "metadata": { "name": "", "signature": "sha256:2b16da5cdf28b881e19c6358b7fa3f2a4148a48f9faa183aa4cd686f1e98627f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "#the point of this notebook is to be able t...	mit
sdpython/code_beatrix	_doc/notebooks/exemples/image_mary_poppins.ipynb	1	1092368	null	mit
luwei0917/awsemmd_script	notebook/Optimization/cys_protein_simulation_analysis_nov12.ipynb	1	10407099	null	mit
carthach/essentia	src/examples/tutorial/example_clickdetector.ipynb	1	462163	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ClickDetector use example\n", "This algorithm detects the locations of impulsive noises (clicks and pops) on\n", "the input audio frame. It relies on LPC coefficients to inverse-filter the\n", "audio in order to attenuate...	agpl-3.0
brianjpetersen/when	develop/re.ipynb	1	5762	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import re\n", "import collections\n", "import datetime" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false ...	mit
GoogleCloudPlatform/asl-ml-immersion	notebooks/kubeflow_pipelines/pipelines/solutions/kfp_pipeline_vertex_automl_batch_predictions.ipynb	1	18089	{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "# Continuous Training with AutoML Vertex Pipelines with Batch Predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Learning Objectives:\n", "1. Learn how to use Vertex...	apache-2.0
bdestombe/SWItest	SWI1D/3cell1.ipynb	1	36884	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SWI - single layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs"...	mit
ldhagen/docker-jupyter	PandasandJupyter.ipynb	1	28268	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Below is from https://dev.socrata.com/blog/2016/02/01/pandas-and-jupyter-notebook.html" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": ...	mit
huilyu2/DataVisualization	project-spring2017/part1/Final-Part1-Trips.ipynb	1	330301	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LIS590DV Final Project - Group Athena\n", "## Part1 - Routes with Different Numbers of Trips\n", "## Part1 - Shapes of Routes with Most/Least Number of Trips\n", "### Author: Hui Lyu" ] }, { "cell_type": "code",...	mit
msschwartz21/craniumPy	experiments/templates/TEMP-landmarks.ipynb	1	6850	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction: Landmarks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import deltascope as ds\n", "import deltascope.alignment as ut\n", "\n", ...	gpl-3.0
mne-tools/mne-tools.github.io	dev/_downloads/1242d47b65d952f9f80cf19fb9e5d76e/35_eeg_no_mri.ipynb	1	7576	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# EEG forwar...	bsd-3-clause
Kadenze/siamese_net	siamese_net_example.ipynb	1	10825704	null	apache-2.0
Centre-Alt-Rendiment-Esportiu/att	notebooks/Serial Ports.ipynb	2	11484	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Serial Ports</h1>\n", "<hr style=\"border: 1px solid #000;\">\n", "<span>\n", "<h2>Serial Port abstraction for ATT.</h2>\n", "</span>\n", "<br>\n", "<span>\n", "This notebook shows the ATT Serial Port ab...	gpl-3.0
pryvkin10x/tsne	examples/iris.ipynb	1	24187	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from tsne import bh_sne" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, ...	bsd-3-clause
mdda/fossasia-2016_deep-learning	notebooks/2-CNN/5-TransferLearning/5-ImageClassifier-keras.ipynb	2	14691	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Re-Purposing a Pretrained Network\n", "\n", "Since a large CNN is very time-consuming to train (even on a GPU), and requires huge amounts of data, is there any way to use a pre-calculated one instead of retraining the whole t...	mit
Joshuaalbert/IonoTomo	src/ionotomo/notebooks/SpectralIndexCalc.ipynb	1	469576	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beams: [ 927.28689232 188.37621839 39.97541645] (arcsec^2)\n", "px/beam: [ 33.64306185 47.0940546 2...	apache-2.0
molgor/spystats	notebooks/.ipynb_checkpoints/model_by_chunks-checkpoint.ipynb	1	10212	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Here I'm process by chunks the entire region." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load Biospytial modules and ...	bsd-2-clause
mforets/polyhedron_tools	examples/asphericity.ipynb	1	62886	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "$\\newcommand{jX}{\\mathcal{X}}$\n", "$\\newcommand{jE}{\\mathcal{E}}$\n", "$\\newcommand{R}{\\mathbb{R}}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Definitions\n", "\n", "- a...	mit
steinam/teacher	jup_notebooks/datenbanken/Sommer_2015.ipynb	1	19667	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Subselects" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "%load_ext sql\n" ] }, { "cell_type": "code"...	mit
caganze/wisps	notebooks/One direction.ipynb	1	28373	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding 145 sources from /Users/caganze/research/splat//resources/Spectra/Public/LRIS-RED/ to spectral database\n", "Adding 89 sourc...	mit
ES-DOC/esdoc-jupyterhub	notebooks/cams/cmip6/models/sandbox-3/ocnbgchem.ipynb	1	79368	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocnbgchem \n", "MIP Era: CMIP6 \n", "Institute: CAMS \n", "Source ID: SANDBOX-3 \n", "**T...	gpl-3.0
dhimmel/SIDER2	compounds/index.ipynb	1	16384	{ "metadata": { "name": "", "signature": "sha256:fa8c70f84ad6b970d91ffdd31b3b00ec6e4cabeb0ceeb79b2938411ab8c0073f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Retrieving PubChem Compound Information...	cc0-1.0
orbitse/data-512-a2	hcds-a2-bias.ipynb	1	195184	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## A2: Bias in data\n", "\n", "### Project Overview\n", "\n", "The goal of this project is to explore the concept of 'bias' in data by analyzing Wikipedia articles on political figures from different countries. \n", ...	mit
obulpathi/datascience	scikit/Chapter 6/Encoding Dictionaries.ipynb	2	2104	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ...	apache-2.0
rsignell-usgs/notebook	wms_sample.ipynb	1	944200	{ "metadata": { "name": "wms_sample" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Exploring Web Map Service (WMS) \n", "\n", "1. WMS and OWSLib\n", "2. Getting some information about t...	mit
mvaz/osqf2015	notebooks/DataPreparation.ipynb	2	2793	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction\n", "Simply the first step to prepare the data for the following notebooks" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "sourc...	mit
GoogleCloudPlatform/vertex-ai-samples	notebooks/community/migration/UJ14 legacy AutoML Vision Video Classification.ipynb	1	38802	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in comp...	apache-2.0
QuantumTechDevStudio/RUDNEVGAUSS	rms/.ipynb_checkpoints/parser_test-Copy1-checkpoint.ipynb	1	303068	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "from pandas.io.json import json_normalize #package for flattening json in pandas df\n", "import numpy as np\n", "import matplotlib.py...	gpl-3.0
Boussau/Notebooks	Notebooks/UniversiFood/Analysis Ademe data.ipynb	1	16485	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "with open(\"Base_Carbone_V16....	gpl-2.0
mssalvador/notebooks	notebooks/cvr/.ipynb_checkpoints/ViewMetaData-checkpoint.ipynb	1	107654	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import sys\n", "\n", "from pyspark import SQLContext\n", "from pyspark import SparkContext\n", "from pyspark.sql.types import...	apache-2.0
vincentadam87/cosyne-visualization	data/data_extraction/.ipynb_checkpoints/poster_counts_only-checkpoint.ipynb	1	7918	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from numpy import ndarray\n", "\n", "# making authors list\n", "def make_authors_list_from_csv(filename):\n", ...	bsd-3-clause
jabooth/menpo-archive	examples/Transforms/Piecewise Affine Transform.ipynb	1	3397	{ "metadata": { "name": "", "signature": "sha256:f8f59a265a6bd4ea04fe9dbea7eedeb48b7c823311bd239190d899da59ee73bd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from menpo.tr...	bsd-3-clause
texib/deeplearning_homework	muki.ipynb	1	268926	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the...	mit
ryan-leung/PHYS4650_Python_Tutorial	notebooks/05-Python-Functions-Class.ipynb	2	11767	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Functions and Classes\n", "\n", "Sometimes you need to define your own functions to work with custom data or solve some problems. A function can be defined with a prefix ``def``. A class is like an umbrella that can co...	bsd-3-clause
gprakhar/janCC	Janacare_Habits_dataset_upto-7May2016.ipynb	1	35209	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Hello World!\n", "This notebook describes the effort filter out users to resurrect with Digital Marketing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clean up data\n", "\n", "de-duplica...	bsd-3-clause
gibiansky/IHaskell	ihaskell-display/ihaskell-juicypixels/test.ipynb	1	1281	{ "cells": [ { "cell_type": "markdown", "metadata": { "hidden": false }, "source": [ "# Notebook test\n", "\n", "This IHaskell noteook should just test, whether IHaskell and JuicyPixels are properly installed and working.\n", "\n", "Just click in the box below and click on the \"R...	mit
james-prior/cohpy	20171027-dojo-collatz-sequence.ipynb	1	2738	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Inspired by [Joe Knapp](mailto:jmknapp at gmail.com)'s \n", "[Collatz conjecture](https://en.wikipedia.org/wiki/Collatz_conjecture)\n", "[email](https://mail.python.org/pipermail/...	mit
cod3licious/simec	09_interpret_similarities_zappos50k.ipynb	1	1338823	null	mit
wcmckee/wcmckee-notebook	wcmgit.ipynb	1	19628	{ "metadata": { "name": "", "signature": "sha256:d46e0179296d90442605354f3dd79bc0b026ce59cf8210e13a48ba9d885890c9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "WCMCKEE GIT\n", "\n", "registe...	gpl-2.0
saudijack/unfpyboot	Day_01/03_Homework/HW2_Survival.ipynb	1	1744	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Homework 2: Survival Driven Development\n", "===========================\n", "\n", "Survival Driven Development (SDD) is the newest software development fad. In this development framework, you specify what the software is ...	mit
sshh12/StockMarketML	backtest/ZiplineSimulator.ipynb	1	97006	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Imports\n", "\n", "from contextlib import contextmanager\n", "from datetime import datetime, timedelta\n", "import sqlite3\n", "import os\n", "\n...	mit
stephenl6705/fluentPy	13. Operator Overloading - Doing It Right.ipynb	1	34429	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Unary Operators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## WHEN X AND +X ARE NOT EQUAL" ] }, { "cell_type": "code", "execution_count": 1, "metadata":...	mit
hetland/python4geosciences	materials/8_beyond_notebook.ipynb	1	46477	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coding outside of Jupyter notebooks\n", "\n", "To be able to run Python on your own computer, I recommend installing [Anaconda](https://www.continuum.io/downloads) which contains basic packages for you to be up and running.\n...	mit
TomTranter/OpenPNM	examples/tutorials/Heat Transfer (1D).ipynb	1	542541	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1D Heat Equation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Heat transfer in 1D is governed by the following PDE (See [here](https://ocw.mit.edu/courses/mathematics/18-303-linear-partial-differe...	mit
hide-tono/python-training	tensorflow-cookbook-keras/ch02/2.9_test_model.ipynb	1	82329	{ "cells": [ { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step #25 A = [[ 6.20395088]]\n", "Loss = 15.9325\n", "Step #50 A = [[ 8.57587433]]\n", "Loss = 1.88605\n", "Step...	apache-2.0
siberianisaev/NeutronBarrel	Python/Neutrons preprocessing/Preprocessing Runner (main).ipynb	1	10722	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from neutron_preprocessing import ExpProcessing\n", "# pandas and numpy libraries are used in neutron_preprocessing " ] }, { "cell_type": "markdown", "metadata": {}, "source": ...	mit
cwharland/data-science-from-scratch	Probability.ipynb	2	6232	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source"...	mit
tensorflow/docs-l10n	site/en-snapshot/addons/tutorials/optimizers_lazyadam.ipynb	2	7623	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2020 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", ...	apache-2.0
ewulczyn/readers	remote_notebooks/traces/Hash Trace IPs.ipynb	1	6214	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import dateutil\n", "import json\n", "from pyspark.sql import SQLContext, Row\n", "sqlContext = SQLConte...	mit
peterwittek/qml-rg	Archiv_Session_Spring_2017/Exercises/05_APS Captcha.ipynb	1	547257	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import keras\n", "import itertools as it\n", "import matplotlib.pyplot as pl\n", "from tempfile import TemporaryDirectory\n", "\n", "TMPDIR = TemporaryDirectory()\n", "k...	gpl-3.0
NYUDataBootcamp/Projects	UG_F16/Srikanth-Bailoor-TV Ratings-Final Project.ipynb	1	1285203	null	mit
robotcator/gensim	gensim Quick Start.ipynb	1	17648	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " # Getting Started with `gensim`\n", " \n", " The goal of this tutorial is to get a new user up-and-running with `gensim`. This notebook covers the following objectives.\n", " \n", " ## Objectives\n", " ...	lgpl-2.1
guilgautier/DPPy	notebooks/fast_sampling_of_beta_ensembles.ipynb	1	341630	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Companion notebook of the paper _Fast sampling of $\\beta$-ensembles_\n", "by [Guillaume Gautier](http://guilgautier.github.io/), [Rémi Bardenet](https://rbardenet.github.io/), and [Michal Valko](http://researchers.lille.inria.fr...	mit
daphnei/nn_chatbot	homeworks/XOR/HW1_report.ipynb	1	13841	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Homework 2\n", "=====\n", "Daphne Ippolito" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import xor_network" ] }, { ...	mit
beibeiyang/ipynbdemo	rAssociationRules.ipynb	2	395428	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demo on Association Rules with R and Jupyter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will install packages arules and arulesViz. \n", "Note the installation may take a while." ...	apache-2.0
luwei0917/awsemmd_script	notebook/Optimization/read_topology_prediction.ipynb	1	40391	{ "cells": [ { "cell_type": "code", "execution_count": 5, "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
reworkhow/XSim.cpp	ReadMe/data.ipynb	2	2320	{ "metadata": { "name": "", "signature": "sha256:5a762ce3a95a8a5b5037f4726d2736803a87ec93b10b00b269562ea472dc65e5" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "> genomoe information including number of ...	gpl-2.0
jason-neal/companion_simulations	Notebooks/Combined_interactive_spectra.ipynb	1	66959	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Interactive Spectra combination!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import ...	mit
wikistat/Apprentissage	Diag-coro/Apprent-R-DiagCoro.ipynb	1	48969	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<center>\n", "<a href=\"http://www.insa-toulouse.fr/\" ><img src=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/logo-insa.jpg\" style=\"float:left; max-width: 120px; display: inline\" alt=\"INSA\"/></a> \n", "\n", ...	gpl-3.0
Upward-Spiral-Science/the-fat-boys	code/Individual_Reports/Edric Tam Updated Report 1.ipynb	1	480408	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Edric Tam Update Fatboy Report:\n", "\n", "1. Focus on spatial information. Take features 3 and 4, the distance and moment of inertia and get hierarchical clustering result. \n", "2. Obtained results that are what we wo...	apache-2.0
neuro-data-science/neuro_data_science	python/setup.ipynb	1	2269	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is used to set up important files for running the notebooks. It will create a \"data\" folder in the root of the repository, and download approximately 60MB of data." ] }, { "cell_type": "code", "execution_co...	gpl-3.0
KIPAC/StatisticalMethods	notes/missingdata.ipynb	1	25838	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Notes: Missing Information, Missing Data, and Selection Effects\n", "\n", "In which we will\n", "* incorporate models for data selection into our toolkit\n", "* ...	gpl-2.0
JuliaPackageMirrors/TypeCheck.jl	doc/TypeCheck.ipynb	3	77069	{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "so...	mit
neurodata/synaptome-stats	collman15v2/201710/runPy.ipynb	1	20693	{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<module 'annoStats' from '/Users/JLP/neurodata-dev/synaptome-stats/collman15v2/201710/annoStats.py'>" ] }, "execution_count": 11, "metadata": {},...	apache-2.0
astro4dev/OAD-Data-Science-Toolkit	Teaching Materials/Programming/Python/Python3Espanol/1_Introduccion/03. Numeros y jerarquía de operaciones.ipynb	1	7640	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Números y jerarquía de operaciones\n", "\n", "- Números enteros y flotantes\n", "- Jerarquía de operaciones\n", "- Asignación de variables\n", "\n", "## Números enteros y flotantes\n", "\n", "\n", ...	gpl-3.0
PerfectoVidal/panda_learning	analisis.ipynb	1	11132	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#importamos numpy y pandas\n", "import numpy as np\n", "import pandas as pd\n", "#cargamos para manejar fechas\n", "import datetime\n", "from datetime...	gpl-3.0
uwkejia/Clean-Energy-Outlook	examples/Extra/Jupyter Notebooks/ZNDX.ipynb	1	82570	{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn import linear_model" ] }, { "cell_type": "code",...	mit
maxalbert/tohu	notebooks/v4/Primitive_generators.ipynb	1	23339	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Primitive generators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook contains tests for tohu's primitive generators." ] }, { "cell_type": "code", "execution_count": 1, "...	mit
bmcfee/librosa	examples/LibROSA audio effects and playback.ipynb	2	43224	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Audio effects and playback with Librosa and IPython Notebook\n", "\n", "This notebook will demonstrate how to do audio effects processing with librosa and IPython notebook. You will need IPython 2.0 or later.\n", "\n", ...	isc
apryor6/apryor6.github.io	visualizations/seaborn/heatmap.ipynb	1	2364555	null	mit
jasemi/Computerphysik-ss17-Uebungen	Uebung-10/Aufgabe1-merlin.ipynb	1	4313	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Aufgabe 1\n", "### Teil a\n", "$$\n", "M =\n", "\\begin{pmatrix}\n", "1 & -1 & 0 & \\cdots & 0 \\\\\n", "-1 & 2 & \\ddots & \\ddots & \\vdots \\\\\n", "0 & \\ddots & \\ddot...	unlicense
mmautner/email_classifier	gmail_importance.ipynb	1	33078	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "# The Task:\n", "\n", "Train a classifier that can predict w...	mit
rafburzy/Python_EE	Visualizations/Glyphs.ipynb	1	1815896	null	bsd-3-clause
fsilva/deputado-histogramado	notebooks/Deputado-Histogramado-3.ipynb	1	359941	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Deputado Histogramado\n", "============\n", "\n", "[expressao.xyz/deputado/](http://expressao.xyz/deputado/)\n", "\n", "Como processar as sessões do parlamento Português" ] }, { "cell_type": "markdown", ...	gpl-3.0
qrsforever/workspace	python/learn/pandas/apply_map_applymap.ipynb	1	2056	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <div align=\"center\">apply,applymap,map</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, {...	mit
dborgesr/Euplotid	pipelines/countsFPKM2DiffExp.ipynb	1	24668	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Take RNA-Seq counts and get differentially expressed genes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Install packages" ] }, { "cell_type": "code", "execution_count": null, "m...	gpl-3.0
TESScience/FPE_Test_Procedures	Evaluating Parameter Interdependence.ipynb	1	9632	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluating Parameter Interdependence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Test run on 10/29/15 by Ed Bokhour. \n", "\n", "Using SD PCB Interface Board serial number 002, SD P...	mit
mne-tools/mne-tools.github.io	stable/_downloads/82d9c13e00105df6fd0ebed67b862464/ssp_projs_sensitivity_map.ipynb	1	2602	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Sensitivit...	bsd-3-clause
sourabhrohilla/ds-masterclass-hands-on	session-2/python/Topic_Model_Recommender.ipynb	1	33316	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Topic Based Recommender" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Topic Based Recommender\n", "1. Represent articles in terms of Topic Vector\n", "2. Represe...	mit
DSSG2017/florence	dev/notebooks/Distributions_MM.ipynb	1	329078	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting distributions\n", "First, import relevant libraries:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import warnings\n", ...	mit
jgarciab/wwd2017	class4/class4_timeseries.ipynb	1	174695	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Working with data 2017. Class 4\n", "## Contact\n", "Javier Garcia-Bernardo\n", "garcia@uva.nl\n", "\n", "## 0. Structure\n", "1. Stats\n", " - Definitions\n", " - What'...	gpl-3.0
MartyWeissman/Python-for-number-theory	P3wNT Notebook 3.ipynb	2	64678	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 3: Lists and the sieve of Eratosthenes in Python 3.x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python provides a powerful set of tools to create and manipulate lists of data. In this par...	gpl-3.0
KatiRG/MethaneEmissions	.ipynb_checkpoints/make_2levelSankey_BUmethaneFile-checkpoint.ipynb	1	44395	{ "cells": [ { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Script to reformat methane data Top-Down file for two-level Sankey\n", "#In BU approach, Sources = methane sources, Targets = regions\n", "\n", "#Output: js...	gpl-3.0
sarvex/PythonMachineLearning	Chapter 3/Pipelines - motivation.ipynb	1	4225	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ...	isc
rasilab/ferrin_elife_2017	scripts/plot_simulation_results_figs_3_to_7.ipynb	1	856557	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Simulation Data Analysis\n", "\n", "<div id=\"toc-wrapper\"><h3> Table of Contents </h3><div id=\"toc\" style=\"max-height: 787px;\"><ol class=\"toc-item\"><li><a href=\"#Global...	gpl-3.0
jgpavez/MedicalDiagnosis	exploratory_analysis.ipynb	1	39770	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory analysis\n", "\n", "This notebook includes some exploratory analysis on the data set for medical diagnosis" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true ...	mit

SyrakuShaikh/python

learning/scientific_computation/Cython/cb_RNG1.ipynb

1

2602

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-06-15T01:49:43.054052Z", "start_time": "2017-06-15T01:49:42.727026Z" }, "collapsed": true }, "outputs": [], "source": [ "%load_ext Cython" ] }, { "cell_type...

gpl-3.0

ericmjl/Network-Analysis-Made-Simple

archive/1-introduction.ipynb

1

8283

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "# Before We Start!\n", "\n", "1. Look at the instructions on the whiteboard.\n", "1. Github repository for these notebooks: **github.com/ericmjl/Network-Analysis-Made-S...

mit

M-R-Houghton/euroscipy_2015

matplotlib/mpl_tutorial.ipynb

1

496538

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EuroSciPy 2015 - matplotlib tutorial" ] }, { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "## Simple plot" ] }, { "cell_type": "markdown", "metadata": {}, "source...

mit

PythonFreeCourse/Notebooks

week07/1_Classes.ipynb

1

85170

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<img src=\"images/logo.jpg\" style=\"display: block; margin-left: auto; margin-right: auto;\" alt=\"לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיז...

mit

taku-y/bmlingam

doc/notebook/tests/test_cli.ipynb

2

18432

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "IPython.notebook.set_autosave_interval(0)" ] }, "metadata": {}, "output_type": "display_data" }, { ...

mit

jamesmarva/maths-with-python

04-basic-plotting.ipynb

3

83290

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are many Python plotting libraries depending on your purpose. However, the standard general-purpose library is `matplotlib`. This is oft...

mit

ellisonbg/talk-2015

12-JupyterLab.ipynb

1

890612

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building Blocks for Interactive Computing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "What are the building blocks for interactive computing?" ] }, { "cell_type": "code", "execution_...

mit

NelisW/ComputationalRadiometry

07-Optical-Sources.ipynb

1

1167644

null

mpl-2.0

nick-youngblut/SIPSim

ipynb/bac_genome/n1147/microBetaDiv_valueRanges.ipynb

1

16797

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Goal\n", "\n", "* Get values ranges for microBetaDiv simulation run\n", " * values for MS" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Setting paths" ] }, { "cell_type": "...

mit

IST256/learn-python

content/lessons/08-Lists/ETEE-Bad-Password-Checker.ipynb

1

2962

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# End-To-End Example: Bad Password Checker\n", "\n", "- Read in list of bad passwords from file `bad-passwords.txt`\n", "- Main program loop which:\n", " - inputs a password \n", " - checks whether the password i...

mit

tensorflow/docs-l10n

site/ko/tutorials/estimator/premade.ipynb

1

21019

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "1Z6Wtb_jisbA" }, "source": [ "##### Copyright 2019 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...

apache-2.0

HSE-LaMBDA/modern-technologies-for-ml-and-big-data

lecture9/Word2Vec.ipynb

1

5853

{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "println(sc.version)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": true }, "outputs": [], "source": [ "va...

mit

SiggyF/notebooks

esmf regrid.ipynb

1

6029

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import os\n", "os.environ['PATH'] = os.environ['PATH'] + ':' + '/home/fedor/Checkouts/esmf/DEFAULTINSTALLDIR/bin/binO/Linux.gfo...

gpl-3.0

celiacintas/dss_practica

ipynb/fit & predict.ipynb

1

5788145

null

gpl-2.0

jnw29/AvrPto_Transcriptome

List_generator_for_genes_meeting_specific_cutoffs_vs_Mock.ipynb

1

12226

{ "metadata": { "name": "", "signature": "sha256:2b16da5cdf28b881e19c6358b7fa3f2a4148a48f9faa183aa4cd686f1e98627f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "#the point of this notebook is to be able t...

mit

sdpython/code_beatrix

_doc/notebooks/exemples/image_mary_poppins.ipynb

1

1092368

null

mit

luwei0917/awsemmd_script

notebook/Optimization/cys_protein_simulation_analysis_nov12.ipynb

1

10407099

null

mit

carthach/essentia

src/examples/tutorial/example_clickdetector.ipynb

1

462163

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ClickDetector use example\n", "This algorithm detects the locations of impulsive noises (clicks and pops) on\n", "the input audio frame. It relies on LPC coefficients to inverse-filter the\n", "audio in order to attenuate...

agpl-3.0

brianjpetersen/when

develop/re.ipynb

1

5762

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import re\n", "import collections\n", "import datetime" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false ...

mit

GoogleCloudPlatform/asl-ml-immersion

notebooks/kubeflow_pipelines/pipelines/solutions/kfp_pipeline_vertex_automl_batch_predictions.ipynb

1

18089

{ "cells": [ { "cell_type": "markdown", "metadata": { "tags": [] }, "source": [ "# Continuous Training with AutoML Vertex Pipelines with Batch Predictions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Learning Objectives:**\n", "1. Learn how to use Vertex...

apache-2.0

bdestombe/SWItest

SWI1D/3cell1.ipynb

1

36884

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SWI - single layer" ] }, { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs"...

mit

ldhagen/docker-jupyter

PandasandJupyter.ipynb

1

28268

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Below is from https://dev.socrata.com/blog/2016/02/01/pandas-and-jupyter-notebook.html" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": ...

mit

huilyu2/DataVisualization

project-spring2017/part1/Final-Part1-Trips.ipynb

1

330301

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LIS590DV Final Project - Group Athena\n", "## Part1 - Routes with Different Numbers of Trips\n", "## Part1 - Shapes of Routes with Most/Least Number of Trips\n", "### Author: Hui Lyu" ] }, { "cell_type": "code",...

mit

msschwartz21/craniumPy

experiments/templates/TEMP-landmarks.ipynb

1

6850

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction: Landmarks" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import deltascope as ds\n", "import deltascope.alignment as ut\n", "\n", ...

gpl-3.0

mne-tools/mne-tools.github.io

dev/_downloads/1242d47b65d952f9f80cf19fb9e5d76e/35_eeg_no_mri.ipynb

1

7576

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

bsd-3-clause

Kadenze/siamese_net

siamese_net_example.ipynb

1

10825704

null

apache-2.0

Centre-Alt-Rendiment-Esportiu/att

notebooks/Serial Ports.ipynb

2

11484

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1>Serial Ports</h1>\n", "<hr style=\"border: 1px solid #000;\">\n", "<span>\n", "<h2>Serial Port abstraction for ATT.</h2>\n", "</span>\n", "<br>\n", "<span>\n", "This notebook shows the ATT Serial Port ab...

gpl-3.0

pryvkin10x/tsne

examples/iris.ipynb

1

24187

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from tsne import bh_sne" ], "language": "python", "metadata": {}, "outputs": [], "prompt_number": 1 }, ...

bsd-3-clause

mdda/fossasia-2016_deep-learning

notebooks/2-CNN/5-TransferLearning/5-ImageClassifier-keras.ipynb

2

14691

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Re-Purposing a Pretrained Network\n", "\n", "Since a large CNN is very time-consuming to train (even on a GPU), and requires huge amounts of data, is there any way to use a pre-calculated one instead of retraining the whole t...

mit

Joshuaalbert/IonoTomo

src/ionotomo/notebooks/SpectralIndexCalc.ipynb

1

469576

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Beams: [ 927.28689232 188.37621839 39.97541645] (arcsec^2)\n", "px/beam: [ 33.64306185 47.0940546 2...

apache-2.0

molgor/spystats

notebooks/.ipynb_checkpoints/model_by_chunks-checkpoint.ipynb

1

10212

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Here I'm process by chunks the entire region." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Load Biospytial modules and ...

bsd-2-clause

mforets/polyhedron_tools

examples/asphericity.ipynb

1

62886

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "$\\newcommand{jX}{\\mathcal{X}}$\n", "$\\newcommand{jE}{\\mathcal{E}}$\n", "$\\newcommand{R}{\\mathbb{R}}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Definitions\n", "\n", "- a...

mit

steinam/teacher

jup_notebooks/datenbanken/Sommer_2015.ipynb

1

19667

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Subselects" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "scrolled": true }, "outputs": [], "source": [ "%load_ext sql\n" ] }, { "cell_type": "code"...

mit

caganze/wisps

notebooks/One direction.ipynb

1

28373

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Adding 145 sources from /Users/caganze/research/splat//resources/Spectra/Public/LRIS-RED/ to spectral database\n", "Adding 89 sourc...

mit

ES-DOC/esdoc-jupyterhub

notebooks/cams/cmip6/models/sandbox-3/ocnbgchem.ipynb

1

79368

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocnbgchem \n", "**MIP Era**: CMIP6 \n", "**Institute**: CAMS \n", "**Source ID**: SANDBOX-3 \n", "**T...

gpl-3.0

dhimmel/SIDER2

compounds/index.ipynb

1

16384

{ "metadata": { "name": "", "signature": "sha256:fa8c70f84ad6b970d91ffdd31b3b00ec6e4cabeb0ceeb79b2938411ab8c0073f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Retrieving PubChem Compound Information...

cc0-1.0

orbitse/data-512-a2

hcds-a2-bias.ipynb

1

195184

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## A2: Bias in data\n", "\n", "### Project Overview\n", "\n", "The goal of this project is to explore the concept of 'bias' in data by analyzing Wikipedia articles on political figures from different countries. \n", ...

mit

obulpathi/datascience

scikit/Chapter 6/Encoding Dictionaries.ipynb

2

2104

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

apache-2.0

rsignell-usgs/notebook

wms_sample.ipynb

1

944200

{ "metadata": { "name": "wms_sample" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Exploring Web Map Service (WMS) \n", "\n", "1. WMS and OWSLib\n", "2. Getting some information about t...

mit

mvaz/osqf2015

notebooks/DataPreparation.ipynb

2

2793

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Introduction\n", "Simply the first step to prepare the data for the following notebooks" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "sourc...

mit

GoogleCloudPlatform/vertex-ai-samples

notebooks/community/migration/UJ14 legacy AutoML Vision Video Classification.ipynb

1

38802

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "# you may not use this file except in comp...

apache-2.0

QuantumTechDevStudio/RUDNEVGAUSS

rms/.ipynb_checkpoints/parser_test-Copy1-checkpoint.ipynb

1

303068

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "from pandas.io.json import json_normalize #package for flattening json in pandas df\n", "import numpy as np\n", "import matplotlib.py...

gpl-3.0

Boussau/Notebooks

Notebooks/UniversiFood/Analysis Ademe data.ipynb

1

16485

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "with open(\"Base_Carbone_V16....

gpl-2.0

mssalvador/notebooks

notebooks/cvr/.ipynb_checkpoints/ViewMetaData-checkpoint.ipynb

1

107654

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import sys\n", "\n", "from pyspark import SQLContext\n", "from pyspark import SparkContext\n", "from pyspark.sql.types import...

apache-2.0

vincentadam87/cosyne-visualization

data/data_extraction/.ipynb_checkpoints/poster_counts_only-checkpoint.ipynb

1

7918

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from numpy import ndarray\n", "\n", "# making authors list\n", "def make_authors_list_from_csv(filename):\n", ...

bsd-3-clause

jabooth/menpo-archive

examples/Transforms/Piecewise Affine Transform.ipynb

1

3397

{ "metadata": { "name": "", "signature": "sha256:f8f59a265a6bd4ea04fe9dbea7eedeb48b7c823311bd239190d899da59ee73bd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "from menpo.tr...

bsd-3-clause

texib/deeplearning_homework

muki.ipynb

1

268926

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python2.7/dist-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the...

mit

ryan-leung/PHYS4650_Python_Tutorial

notebooks/05-Python-Functions-Class.ipynb

2

11767

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Functions and Classes\n", "\n", "Sometimes you need to define your own functions to work with custom data or solve some problems. A function can be defined with a prefix ``def``. A class is like an umbrella that can co...

bsd-3-clause

gprakhar/janCC

Janacare_Habits_dataset_upto-7May2016.ipynb

1

35209

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Hello World!\n", "This notebook describes the effort filter out users to resurrect with Digital Marketing" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Clean up data\n", "\n", "de-duplica...

bsd-3-clause

gibiansky/IHaskell

ihaskell-display/ihaskell-juicypixels/test.ipynb

1

1281

{ "cells": [ { "cell_type": "markdown", "metadata": { "hidden": false }, "source": [ "# Notebook test\n", "\n", "This IHaskell noteook should just test, whether IHaskell and JuicyPixels are properly installed and working.\n", "\n", "Just click in the box below and click on the \"R...

mit

james-prior/cohpy

20171027-dojo-collatz-sequence.ipynb

1

2738

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "Inspired by [Joe Knapp](mailto:jmknapp at gmail.com)'s \n", "[Collatz conjecture](https://en.wikipedia.org/wiki/Collatz_conjecture)\n", "[email](https://mail.python.org/pipermail/...

mit

cod3licious/simec

09_interpret_similarities_zappos50k.ipynb

1

1338823

null

mit

wcmckee/wcmckee-notebook

wcmgit.ipynb

1

19628

{ "metadata": { "name": "", "signature": "sha256:d46e0179296d90442605354f3dd79bc0b026ce59cf8210e13a48ba9d885890c9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "WCMCKEE GIT\n", "\n", "registe...

gpl-2.0

saudijack/unfpyboot

Day_01/03_Homework/HW2_Survival.ipynb

1

1744

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Homework 2: Survival Driven Development\n", "===========================\n", "\n", "Survival Driven Development (SDD) is the newest software development fad. In this development framework, you specify what the software is ...

mit

sshh12/StockMarketML

backtest/ZiplineSimulator.ipynb

1

97006

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [], "source": [ "# Imports\n", "\n", "from contextlib import contextmanager\n", "from datetime import datetime, timedelta\n", "import sqlite3\n", "import os\n", "\n...

mit

stephenl6705/fluentPy

13. Operator Overloading - Doing It Right.ipynb

1

34429

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Unary Operators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## WHEN X AND +X ARE NOT EQUAL" ] }, { "cell_type": "code", "execution_count": 1, "metadata":...

mit

hetland/python4geosciences

materials/8_beyond_notebook.ipynb

1

46477

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coding outside of Jupyter notebooks\n", "\n", "To be able to run Python on your own computer, I recommend installing [Anaconda](https://www.continuum.io/downloads) which contains basic packages for you to be up and running.\n...

mit

TomTranter/OpenPNM

examples/tutorials/Heat Transfer (1D).ipynb

1

542541

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 1D Heat Equation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Heat transfer in 1D is governed by the following PDE (See [here](https://ocw.mit.edu/courses/mathematics/18-303-linear-partial-differe...

mit

hide-tono/python-training

tensorflow-cookbook-keras/ch02/2.9_test_model.ipynb

1

82329

{ "cells": [ { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Step #25 A = [[ 6.20395088]]\n", "Loss = 15.9325\n", "Step #50 A = [[ 8.57587433]]\n", "Loss = 1.88605\n", "Step...

apache-2.0

siberianisaev/NeutronBarrel

Python/Neutrons preprocessing/Preprocessing Runner (main).ipynb

1

10722

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from neutron_preprocessing import ExpProcessing\n", "# pandas and numpy libraries are used in neutron_preprocessing " ] }, { "cell_type": "markdown", "metadata": {}, "source": ...

mit

cwharland/data-science-from-scratch

Probability.ipynb

2

6232

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source"...

mit

tensorflow/docs-l10n

site/en-snapshot/addons/tutorials/optimizers_lazyadam.ipynb

2

7623

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2020 The TensorFlow Authors.\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", ...

apache-2.0

ewulczyn/readers

remote_notebooks/traces/Hash Trace IPs.ipynb

1

6214

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2\n", "import dateutil\n", "import json\n", "from pyspark.sql import SQLContext, Row\n", "sqlContext = SQLConte...

mit

peterwittek/qml-rg

Archiv_Session_Spring_2017/Exercises/05_APS Captcha.ipynb

1

547257

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import keras\n", "import itertools as it\n", "import matplotlib.pyplot as pl\n", "from tempfile import TemporaryDirectory\n", "\n", "TMPDIR = TemporaryDirectory()\n", "k...

gpl-3.0

NYUDataBootcamp/Projects

UG_F16/Srikanth-Bailoor-TV Ratings-Final Project.ipynb

1

1285203

null

mit

robotcator/gensim

gensim Quick Start.ipynb

1

17648

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " # Getting Started with `gensim`\n", " \n", " The goal of this tutorial is to get a new user up-and-running with `gensim`. This notebook covers the following objectives.\n", " \n", " ## Objectives\n", " ...

lgpl-2.1

guilgautier/DPPy

notebooks/fast_sampling_of_beta_ensembles.ipynb

1

341630

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Companion notebook of the paper _Fast sampling of $\\beta$-ensembles_\n", "by [Guillaume Gautier](http://guilgautier.github.io/), [Rémi Bardenet](https://rbardenet.github.io/), and [Michal Valko](http://researchers.lille.inria.fr...

mit

daphnei/nn_chatbot

homeworks/XOR/HW1_report.ipynb

1

13841

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Homework 2\n", "=====\n", "Daphne Ippolito" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import xor_network" ] }, { ...

mit

beibeiyang/ipynbdemo

rAssociationRules.ipynb

2

395428

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demo on Association Rules with R and Jupyter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we will install packages arules and arulesViz. \n", "Note the installation may take a while." ...

apache-2.0

luwei0917/awsemmd_script

notebook/Optimization/read_topology_prediction.ipynb

1

40391

{ "cells": [ { "cell_type": "code", "execution_count": 5, "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

reworkhow/XSim.cpp

ReadMe/data.ipynb

2

2320

{ "metadata": { "name": "", "signature": "sha256:5a762ce3a95a8a5b5037f4726d2736803a87ec93b10b00b269562ea472dc65e5" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "> genomoe information including number of ...

gpl-2.0

jason-neal/companion_simulations

Notebooks/Combined_interactive_spectra.ipynb

1

66959

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Interactive Spectra combination!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from __future__ import ...

mit

wikistat/Apprentissage

Diag-coro/Apprent-R-DiagCoro.ipynb

1

48969

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<center>\n", "<a href=\"http://www.insa-toulouse.fr/\" ><img src=\"http://www.math.univ-toulouse.fr/~besse/Wikistat/Images/logo-insa.jpg\" style=\"float:left; max-width: 120px; display: inline\" alt=\"INSA\"/></a> \n", "\n", ...

gpl-3.0

Upward-Spiral-Science/the-fat-boys

code/Individual_Reports/Edric Tam Updated Report 1.ipynb

1

480408

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**Edric Tam Update Fatboy Report:**\n", "\n", "1. Focus on spatial information. Take features 3 and 4, the distance and moment of inertia and get hierarchical clustering result. \n", "2. Obtained results that are what we wo...

apache-2.0

neuro-data-science/neuro_data_science

python/setup.ipynb

1

2269

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook is used to set up important files for running the notebooks. It will create a \"data\" folder in the root of the repository, and download approximately 60MB of data." ] }, { "cell_type": "code", "execution_co...

gpl-3.0

KIPAC/StatisticalMethods

notes/missingdata.ipynb

1

25838

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Notes: Missing Information, Missing Data, and Selection Effects\n", "\n", "In which we will\n", "* incorporate models for data selection into our toolkit\n", "* ...

gpl-2.0

JuliaPackageMirrors/TypeCheck.jl

doc/TypeCheck.ipynb

3

77069

{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Introduction" ] }, { "cell_type": "markdown", "metadata": {}, "so...

mit

neurodata/synaptome-stats

collman15v2/201710/runPy.ipynb

1

20693

{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "<module 'annoStats' from '/Users/JLP/neurodata-dev/synaptome-stats/collman15v2/201710/annoStats.py'>" ] }, "execution_count": 11, "metadata": {},...

apache-2.0

astro4dev/OAD-Data-Science-Toolkit

Teaching Materials/Programming/Python/Python3Espanol/1_Introduccion/03. Numeros y jerarquía de operaciones.ipynb

1

7640

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Números y jerarquía de operaciones\n", "\n", "- Números enteros y flotantes\n", "- Jerarquía de operaciones\n", "- Asignación de variables\n", "\n", "## Números enteros y flotantes\n", "\n", "\n", ...

gpl-3.0

PerfectoVidal/panda_learning

analisis.ipynb

1

11132

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#importamos numpy y pandas\n", "import numpy as np\n", "import pandas as pd\n", "#cargamos para manejar fechas\n", "import datetime\n", "from datetime...

gpl-3.0

uwkejia/Clean-Energy-Outlook

examples/Extra/Jupyter Notebooks/ZNDX.ipynb

1

82570

{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from sklearn import linear_model" ] }, { "cell_type": "code",...

mit

maxalbert/tohu

notebooks/v4/Primitive_generators.ipynb

1

23339

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Primitive generators" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook contains tests for tohu's primitive generators." ] }, { "cell_type": "code", "execution_count": 1, "...

mit

bmcfee/librosa

examples/LibROSA audio effects and playback.ipynb

2

43224

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Audio effects and playback with Librosa and IPython Notebook\n", "\n", "This notebook will demonstrate how to do audio effects processing with librosa and IPython notebook. You will need IPython 2.0 or later.\n", "\n", ...

isc

apryor6/apryor6.github.io

visualizations/seaborn/heatmap.ipynb

1

2364555

null

mit

jasemi/Computerphysik-ss17-Uebungen

Uebung-10/Aufgabe1-merlin.ipynb

1

4313

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Aufgabe 1\n", "### Teil a\n", "$$\n", "M =\n", "\\begin{pmatrix}\n", "1 & -1 & 0 & \\cdots & 0 \\\\\n", "-1 & 2 & \\ddots & \\ddots & \\vdots \\\\\n", "0 & \\ddots & \\ddot...

unlicense

mmautner/email_classifier

gmail_importance.ipynb

1

33078

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "# The Task:\n", "\n", "Train a classifier that can predict w...

mit

rafburzy/Python_EE

Visualizations/Glyphs.ipynb

1

1815896

null

bsd-3-clause

fsilva/deputado-histogramado

notebooks/Deputado-Histogramado-3.ipynb

1

359941

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Deputado Histogramado\n", "============\n", "\n", "[expressao.xyz/deputado/](http://expressao.xyz/deputado/)\n", "\n", "Como processar as sessões do parlamento Português" ] }, { "cell_type": "markdown", ...

gpl-3.0

qrsforever/workspace

python/learn/pandas/apply_map_applymap.ipynb

1

2056

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <div align=\"center\">apply,applymap,map</div>" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd" ] }, {...

mit

dborgesr/Euplotid

pipelines/countsFPKM2DiffExp.ipynb

1

24668

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Take RNA-Seq counts and get differentially expressed genes" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Install packages" ] }, { "cell_type": "code", "execution_count": null, "m...

gpl-3.0

TESScience/FPE_Test_Procedures

Evaluating Parameter Interdependence.ipynb

1

9632

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Evaluating Parameter Interdependence" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Test run on 10/29/15 by Ed Bokhour. \n", "\n", "Using SD PCB Interface Board serial number 002, SD P...

mit

mne-tools/mne-tools.github.io

stable/_downloads/82d9c13e00105df6fd0ebed67b862464/ssp_projs_sensitivity_map.ipynb

1

2602

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

bsd-3-clause

sourabhrohilla/ds-masterclass-hands-on

session-2/python/Topic_Model_Recommender.ipynb

1

33316

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Topic Based Recommender" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Topic Based Recommender\n", "1. Represent articles in terms of Topic Vector\n", "2. Represe...

mit

DSSG2017/florence

dev/notebooks/Distributions_MM.ipynb

1

329078

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Plotting distributions\n", "First, import relevant libraries:" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import warnings\n", ...

mit

jgarciab/wwd2017

class4/class4_timeseries.ipynb

1

174695

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Working with data 2017. Class 4\n", "## Contact\n", "Javier Garcia-Bernardo\n", "garcia@uva.nl\n", "\n", "## 0. Structure\n", "1. Stats\n", " - Definitions\n", " - What'...

gpl-3.0

MartyWeissman/Python-for-number-theory

P3wNT Notebook 3.ipynb

2

64678

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Part 3: Lists and the sieve of Eratosthenes in Python 3.x" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python provides a powerful set of tools to create and manipulate lists of data. In this par...

gpl-3.0

KatiRG/MethaneEmissions

.ipynb_checkpoints/make_2levelSankey_BUmethaneFile-checkpoint.ipynb

1

44395

{ "cells": [ { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Script to reformat methane data Top-Down file for two-level Sankey\n", "#In BU approach, Sources = methane sources, Targets = regions\n", "\n", "#Output: js...

gpl-3.0

sarvex/PythonMachineLearning

Chapter 3/Pipelines - motivation.ipynb

1

4225

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

isc

rasilab/ferrin_elife_2017

scripts/plot_simulation_results_figs_3_to_7.ipynb

1

856557

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Simulation Data Analysis\n", "\n", "<div id=\"toc-wrapper\"><h3> Table of Contents </h3><div id=\"toc\" style=\"max-height: 787px;\"><ol class=\"toc-item\"><li><a href=\"#Global...

gpl-3.0

jgpavez/MedicalDiagnosis

exploratory_analysis.ipynb

1

39770

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Exploratory analysis\n", "\n", "This notebook includes some exploratory analysis on the data set for medical diagnosis" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true ...

mit