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
goedman/RobGoedmansNotebooks.jl	notebooks/SheehanOlver/02.ipynb	1	6699	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# What is a string?\n", "\n", "Strings can be created with quotation marks" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { ...	mit
opengridcc/opengrid	notebooks/Multi-variable Linear Regression Demo.ipynb	1	65888	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi-variable linear regression\n", "\n", "The multivariable linear regression analysis is used to create a model of a single variable, typically an energy consumption. We call this the dependent variable. The model is co...	apache-2.0
woobe/h2o_tutorials	introduction_to_machine_learning/py_04b_classification_ensembles.ipynb	1	26781	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning with H2O - Tutorial 4b: Classification Models (Ensembles)\n", "\n", "<hr>\n", "\n", "Objective:\n", "\n", "- This tutorial explains how to create stacked ensembles of classification models...	mit
GoogleCloudPlatform/asl-ml-immersion	notebooks/feature_engineering/solutions/5_tftransform_taxifare.ipynb	1	81069	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TfTransform # \n", "\n", "\n", "\n", "\n", "Learning Objectives\n", "1. Preproccess data and engineer new features using TfTransform \n", "1. Create and deploy Apache Beam pipeline \n", "1. Use pro...	apache-2.0
BoasWhip/Black	Notebook/M269 Unit 4 Notes -- Search.ipynb	1	36431	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Searching" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.1 Searching Lists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Algorithm: Selection\n", ...	mit
CompPhysics/ComputationalPhysics2	doc/Projects/2019/Project2CC/ipynb/Project2CC.ipynb	1	23306	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!-- dom:TITLE: Project 2, Coupled cluster studies of electronic systems. Deadline May 31 -->\n", "# Project 2, Coupled cluster studies of electronic systems. Deadline May 31\n", "<!-- dom:AUTHOR: [Computational Physics II FYS4...	cc0-1.0
hendrycks/fooling	MNIST/show_reconstructions.ipynb	1	37082	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n...	mit
aschaffn/phys202-2015-work	assignments/assignment02/ProjectEuler2.ipynb	1	2080	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 2" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Each new term in the Fibonacci sequence is generated by adding the previous two ...	mit
CartoDB/cartoframes	docs/examples/data_visualization/layers/add_multiple_layers.ipynb	1	177463	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Add Multiple Layers\n", "\n", "In this example, three Layers are added to a Map. Notice the draw order and default symbology for each.\n", "\n", "For more information, run `help(Layer)`" ] }, { "cell_type":...	bsd-3-clause
kenjisato/intro-macro	doc/python/Optimal Growth (Euler).ipynb	1	85296	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Computing the Optimal Grwoth Model by the Euler Equation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "i...	mit
ccasotto/rmtk	rmtk/vulnerability/derivation_fragility/equivalent_linearization/lin_miranda_2008/lin_miranda_2008.ipynb	2	12031	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lin and Miranda (2008)\n", "\n", "This method, described in [Lin and Miranda (2008)](http://dx.doi.org/10.1061/%28ASCE%290733-9445%282008%29134:11%281685%29), estimates the maximum inelastic displacement of an existing struct...	agpl-3.0
GoogleCloudPlatform/asl-ml-immersion	notebooks/launching_into_ml/solutions/1_explore_data.ipynb	1	239477	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Explore and create ML datasets\n", "\n", "Learning Objectives\n", "* Access and explore a public BigQuery dataset on NYC Taxi Cab rides\n", "* Visualize your dataset using the Seaborn library\n", "* Inspect an...	apache-2.0
icoxfog417/tensorflow-arithmetic	tensorflow-arithmetic.ipynb	1	3287	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Arithmetic\n", "\n", "\n", "## calculation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output...	mit
bpgc-cte/python2017	Week 2/Practice Set 1.ipynb	1	4638	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Practice Questions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " 1. Take two input strings and print out every odd indexed character of first string and then\n", " followed ...	mit
fangohr/oommf-python	new/notebooks/fmr_standard_problem.ipynb	1	210213	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# FMR standard problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Author: Marijan Beg\n", "\n", "Date: 11 May 2016" ] }, { "cell_type": "markdown", "metadata": {}, ...	bsd-2-clause
frodo81/EnergyMonitor	emNotebook.ipynb	2	1662776	null	gpl-2.0
gangadhara691/gangadhara691.github.io	P3 wrangle_data/DataWrangling_ganga.ipynb	1	30449	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# P3 - Data Wrangling with MongoDB\n", "# OpenStreetMap Project Data Wrangling with MongoDB\n", "## Gangadhara Naga Sai<a name=\"top\"></a>\n", "Data used -<a href=https://mapzen.com/metro-extracts/> MapZen Weekly O...	mit
probml/pyprobml	notebooks/book1/10/iris_logreg.ipynb	1	316	{ "cells": [ { "cell_type": "markdown", "id": "75544f56", "metadata": {}, "source": [ "Source of this notebook is here: https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/02/iris_logreg.ipynb" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }	mit
SumitDutta1997/Toy-Projects.	Tic_Tac_Toe.ipynb	1	5108	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TIC TAC TOE\n", "\n", "from IPython.display import clear_output\n", "\n", "def display_board(board):\n", " clear_output()\n", " \n", ...	mit
hschilling/learning_ipython	complex_step_method-log_plot.ipynb	1	54041	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Taken from http://kitchingroup.cheme.cmu.edu/blog/2013/02/27/A-novel-way-to-numerically-estimate-the-derivative-of-a-function-comple...	mit
taliamo/Final_Project	organ_pitch/Scripts/.ipynb_checkpoints/upload_env_data-checkpoint.ipynb	1	91647	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## T. Martz-Oberlander, 2015-11-12, CO2 and Speed of Sound\n", "### Formatting ENVIRONMENTAL CONDITIONS pipe organ data for Python operations\n", "\n", "NOTE: Here, pitch and frequency are used interchangeably to signify th...	mit
datahac/jup	.ipynb_checkpoints/helper_weather_dairy-checkpoint.ipynb	1	20297	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Weather API connect" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTA...	apache-2.0
eds-uga/csci1360e-su16	lectures/L9.ipynb	1	25388	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Lecture 9: Introduction to Functions\n", "\n", "CSCI 1360E: Foundations for Informatics and Analytics" ] }, { "cell_type": "markdown", "metadata": { "sl...	mit
digitaldingo/task_force	Studiepraktikdag - dataanalyse.ipynb	1	101102	{ "metadata": { "name": "", "signature": "sha256:74d813854f2e100d25c35670aaefd23fe6210310678b27a0160dc53dc264d447" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "from ekstra_funkti...	mit
drvinceknight/gt	nbs/other/Schedule.ipynb	1	1460	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Approximate weekly schedule\n", "\n", "- Week 1:\n", " - About the course\n", " - Normal form games\n", "- Week 2:\n", " - Calculating utilities of strategies\n", " - Rationalisation\n", "-...	mit
rasbt/python-machine-learning-book	code/ch12/ch12.ipynb	1	1876868	null	mit
zhongdai/learn-scikit	ch02_svm_image_classifier.ipynb	1	2139958	null	mit
ES-DOC/esdoc-jupyterhub	notebooks/test-institute-3/cmip6/models/sandbox-1/land.ipynb	1	173522	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "MIP Era: CMIP6 \n", "Institute: TEST-INSTITUTE-3 \n", "Source ID: SANDBOX-1 \n", ...	gpl-3.0
jhartford/pybo	pybo - pygp tests.ipynb	1	16076	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pygp\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ ...	bsd-2-clause
idc9/law-net	vertex_metrics_experiment/chalboards/test_revised_pipeline.ipynb	1	5957	{ "cells": [ { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] ...	mit
CodeNeuro/notebooks	worker/notebooks/thunder/tutorials/factorization.ipynb	11	622198	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Factorization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Factorization algorithms are useful for data-driven decomposition of spatial and temporal data, for example, to recover spatial patterns ...	mit
wheeler-microfluidics/teensy-minimal-rpc	teensy_minimal_rpc/notebooks/dma-examples/Example - [BROKEN] Periodic multi-channel ADC multiple samples using DMA and PIT.ipynb	1	32444	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# __NB__ Cannot use PIT to trigger periodic DMA due to hardware bug\n", "\n", "See [here][1].\n", "\n", "Try using PDB instead??\n", "\n", "[1]: https://community.freescale.com/thread/102320#112176" ] }, ...	gpl-3.0
antoniomezzacapo/qiskit-tutorial	community/games/game_engines/Making_your_own_hello_quantum.ipynb	1	306391	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hello Quantum for Jupyter notebook\n", "\n", "[Hello Quantum](https://helloquantum.mybluemix.net/) is a project based on the idea of visualizing two qubit states and gates, and making them accessible to a non-specialist audie...	apache-2.0
agaveapi/SC17-container-tutorial	content/notebooks/Introduction.ipynb	1	14393	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HPC via HTTP: Portable, Scalable Computing using App Containers and the Agave API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Date: Monday, Nov 13th \n", "Time: 8:30am - 12:00pm \n"...	bsd-3-clause
root-mirror/training	SummerStudentCourse/2019/Exercises/ROOTBooks/GausFit_Solution.ipynb	4	83811	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise: Gauss Histogram Fitting \n", "\n", "This example shows how to fit an histogram using ROOT " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create First an empty histogram with 50 b...	gpl-2.0
anonymous-ijcai/dsw-ont-ijcai	dswont/topics_music.ipynb	1	66610	{ "metadata": { "name": "", "signature": "sha256:4d8dd393c2552c4aa372b9b760eb57a8e00f9d67cac2562512edf8ecf8ce642f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# %matplotlib inline\n", "# %load_ext...	gpl-3.0
eco32i/biodata	sessions/templates/RNA-Seq/06 - PCA.ipynb	1	4999	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true, "tags": [] }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, ...	mit
arcyfelix/Courses	17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/03-General Pandas/02-Series.ipynb	2	8521	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "\n", "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n", "___\n", "# Series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first main dat...	apache-2.0
ramseylab/networkscompbio	class21_reveal_python3.ipynb	1	8356	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Class 21: joint entropy and the REVEAL algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the bladder cancer gene expression data to test out the REVEAL algorithm. First, we'll load...	apache-2.0
mne-tools/mne-tools.github.io	0.19/_downloads/4b44551162dc4f8dda6c7f0d2af501fe/plot_10_reading_meg_data.ipynb	1	13295	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Importing ...	bsd-3-clause
elenduuche/deep-learning	seq2seq/sequence_to_sequence_implementation.ipynb	1	521478	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Character Sequence to Sequence \n", "In this notebook, we'll build a model that takes in a sequence of letters, and outputs a sorted version of that sequence. We'll do that using what we've learned so fa...	mit
rdempsey/web-scraping-data-mining-course	week8/2_bi_dashboards_reporting/4 - Create and Save the Plots for the Dashboard.ipynb	1	5433	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import the Python libraries we need\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib\n", "matplotlib.use('Agg')\n", "impor...	mit
phockett/ePSproc	notebooks/plottingDev/hvPlot_Holoviews_examples_140720.ipynb	1	2943775	null	gpl-3.0
jllanfranchi/pygeneric	pdfmerge.ipynb	1	6606	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import re\n", "import numpy as np\n", "from IPython.display import display as D\n", "%rehashx" ], "langua...	mit
lucduff/Recommend_Top_5_Thai_Restaurants_in_Las_Vegas	Recommender-Models/recommender_with_ratings.ipynb	1	44182182	null	mit
dream-olfaction/olfaction-prediction	opc_python/hulab/collaboration/scoring and graphs.ipynb	1	205908	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "compares the predicted results to the true results and calculates the scores \n", "creates the graph" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], ...	mit
swails/mdtraj	examples/principal-components.ipynb	6	4194	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "scikit-learn is a machine learning library for python, with a very easy to use API and great documentation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": ...	lgpl-2.1
jan-rybizki/Galaxia_wrap	notebook/.ipynb_checkpoints/[4]mag_limited_survey_function-checkpoint.ipynb	1	18223	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import os, sys\n", "path = os.path.abspath('../library/')\n", "if path not in sys.path:\n", " sys.path.append(path)\n", "fr...	mit
DamienIrving/ocean-analysis	development/EBAF-TOA.ipynb	1	607621	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## CERES TOA data\n", "\n", "Playing around with the CERES EBAF-TOA Ed4.0 net TOA all-sky radiative flux data downloaded from [here](https://ceres-tool.larc.nasa.gov/ord-tool/jsp/...	mit
marcinofulus/teaching	ML_SS2017/numpy_kto_szybciej.ipynb	1	42801	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np " ] }, { "cell_type": "code", "execution_count": 2, "metadata": {...	gpl-3.0
ajaybhat/DLND	Project 1/Project-1.ipynb	1	479418	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Your first neural network\n", "\n", "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural networ...	apache-2.0
EducationalTestingService/rsmtool	rsmtool/notebooks/summary/header.ipynb	1	7881	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Setting options for the plots\n", "%matplotlib inline\n", "%config InlineBackend.figure_formats={'retina', 'svg'}\n", "%config InlineBackend.rc={'savefig...	apache-2.0
ES-DOC/esdoc-jupyterhub	notebooks/pcmdi/cmip6/models/sandbox-3/land.ipynb	1	173500	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "MIP Era: CMIP6 \n", "Institute: PCMDI \n", "Source ID: SANDBOX-3 \n", "**Topic...	gpl-3.0
janisz/bus-factor-calculator	marathon-consul/Calculate factor.ipynb	1	52720	{ "cells": [ { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import networkx as nx" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "o...	mit
mne-tools/mne-tools.github.io	0.19/_downloads/a1ab4842a5aa341564b4fa0a6bf60065/plot_dipole_orientations.ipynb	1	13155	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\nThe role of ...	bsd-3-clause
kriukov/interval-methods	ipynb/henon_points.ipynb	2	37247	{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ ";cd .." ], "language": "python", "metadata": {}, "outputs": [ { "output_type"...	gpl-3.0
cavestruz/MLPipeline	notebooks/anomaly_detection/sample_anomaly_detection.ipynb	1	7074	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Let us first explore an example that falls under novelty detection. Here, we train a model on data with some distribution and no outliers. The test data, has some \"novel\" subset of data that does not follow that distribution." ]...	mit
mzszym/oedes	examples/scl/scl-trapping.ipynb	1	75101	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Steady-state space-charge-limited current with traps \n", "\n", "This example shows how to simulate effects of a single trap level on current-voltage characteristics of a single carrier device.\n", "\n" ] }, { "...	agpl-3.0
autumn-lake/TalkingData-Mobile-User-Demographics	ipynb_notebooks/brand_model_predictions_train-Copy1.ipynb	1	72432	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import xgboost as xgb\n", "%matplotlib inline\n", "#import seaborn as sns\n", "import matplotlib.pyp...	mit
bioinformatica-corso/2014-15	laboratorio/lezione18-17dic21/esercizio1-pysam.ipynb	3	4291	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Esercizio1 - Pysam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prendere in input un BAM file che contiene allineamenti ad un'unica reference (cromosoma X) e:\n", "\n", "- controllare se s...	cc0-1.0
stereoboy/Study	papers/Attention_Models/Attention Models.ipynb	1	1475	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Hard Attention VS Soft Attention\n", "\n", "* Hard Attention\n", " * Chose Attention spots (or locations) using Stochastical methods\n", " * Use Reinforcement Learning\n", " * e.g. Re...	mit
ptpro3/ptpro3.github.io	Projects/Project2/Project2_Prashant.ipynb	1	950138	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "Project: Project 2: Luther\n", "Date: 02/03/2017\n", "Name: Prashant Tatineni\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project Overview\n", "For ...	mit
SIMEXP/Projects	preventad/.ipynb_checkpoints/preventad_scores_dynamic-checkpoint.ipynb	1	317033	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, quick outline:\n", "1. Have the network to left\n", "2. Have the network time series top center\n", "3. Take three time windows\n", "4. Have the seed map and winner takes all for each window in stack mid/bottom cent...	mit
PyLCARS/PythonUberHDL	myHDL_DigLogicFundamentals/myHDL_Combinational/Multiplexers(MUX).ipynb	1	312770	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\\title{myHDL Combinational Logic Elements: Multiplexers (MUXs))}\n", "\\author{Steven K Armour}\n", "\\maketitle" ] }, { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table o...	bsd-3-clause
dfm/emcee3	docs/user/parallel.ipynb	1	7701	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parallelization\n", "===============\n", "\n", "emcee supports parallelization ...	mit
whitead/numerical_stats	unit_7/hw_2018/Homework_7_Key.ipynb	1	111431	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Homework 7 Key\n", "====\n", "#### CHE 116: Numerical Methods and Statistics\n", "\n", "3/8/2018\n", "\n", "----" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": ...	gpl-3.0
thewtex/SimpleITK-Notebooks	62_Registration_Tuning.ipynb	1	17093	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align=\"center\">Registration Settings: Choices, Choices, Choices</h1>\n", "\n", "The performance of most registration algorithms is dependent on a large number of parameter settings. For optimal performance you will need t...	apache-2.0
fmonti/mgcnn	Notebooks/douban/.ipynb_checkpoints/supervised_approach_douban_dataset_original_training_set_norm_acc_with_factorization_1_mono_conv_norm_W-checkpoint.ipynb	1	1585447	null	gpl-3.0
park-python/course	lectures/10_Python_Applications/pandas_tutorial.ipynb	1	43170	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Скачаем датасет матчей Испанской Ла Лиги за последние 90 лет\n",...	bsd-3-clause
alephcero/adsProject	olds/DataAnalysisPlanA.ipynb	1	82434	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# New York University\n", "## Applied Data Science 2016 Final Project\n", "----\n", "## Measuring household income under Redatam in CensusData\n", "\n", "Project Description: Lorem ipsum\n", "\n", "**Mem...	gpl-3.0
Rlee13/pyPLY	docs/examples/GraphiteEpoxy3LayersPolarPlot.ipynb	1	53312	{ "metadata": { "name": "GraphiteEpoxy3LayersPolarPlot" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "# Example of polar plot for GraphiteEpoxy (3x layers)\n", "# with dashed lines the theoretical quasi-i...	lgpl-2.1
m-walters/mcmd	rodplot.ipynb	1	435499	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as pypatch\n", "import time\n", "%matplotlib inline\n", "\n", "cos...	mit
charlielito/supervised-basico-admission-exams	python-sample.ipynb	2	4493151	null	mit
talespaiva/folium	examples/Features.ipynb	1	337930	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This is to import the repository's version of folium ; not the installed one.\n", "import sys, os\n", "sys.path.insert(0,'..')" ] }, { "cell_type": "c...	mit
bloomberg/bqplot	examples/Interactions/Interaction Layer.ipynb	2	25483	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "symbol = \"Security 1\"\n", "symbol2 = \"Security 2\"" ] }, { "cell_type": "code", "execution_count": null, ...	apache-2.0
crystalzhaizhai/cs207_yi_zhai	homeworks/HW6/HW6_finished.ipynb	1	64550	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework 6\n", "## Due: Tuesday, October 10 at 11:59 PM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Problem 1: Bank Account Revisited\n", "\n", "We are going to rewrite the bank ac...	mit
dm-wyncode/zipped-code	error_files/embrace_hug.ipynb	1	5583	{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import hug\n", "import webcolors" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "...	mit
VUInformationRetrieval/IR2016_2017	04_analysis.ipynb	1	51342	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mini-Assignment 4: Link Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this mini-assignment, we will exploit graph algorithms to improve search results. For our dataset of scientific pap...	gpl-2.0
shashank14/Asterix	1-Python Crash course/Python-Crash-Course/Python Crash Course Exercises .ipynb	1	12060	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Crash Course Exercises \n", "\n", "This is an optional exercise to test your understanding of Python Basics. If you find this extremely challenging, then you probably are not ready for the rest of this course yet and d...	apache-2.0
chusine/dlnd	autoencoder/Convolutional_Autoencoder.ipynb	1	89430	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Convolutional Autoencoder\n", "\n", "Sticking with the MNIST dataset, let's improve our autoencoder's performance using convolutional layers. Again, loading modules and the data." ] }, { "cell_type": "code", "exe...	mit
OSGeoLabBp/tutorials	english/data_processing/lessons/ml_clustering.ipynb	1	184051	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "ml_clustering.ipynb", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyP1OxpHVNY5kooCRkzf55Us", "include_colab_link": true }, "kernelspec": { "name": "python3", "displa...	cc0-1.0
zsedem/haskell-playground	Notebooks/JiraActivtiesRSSpoc.ipynb	1	19978	{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "{-# LANGUAGE OverloadedStrings, TemplateHaskell, DisambiguateRecordFields #-}\n", "import Data.Maybe\n", "import Control.Monad\n", "import Text.Feed.Import\n...	mit
HEROES-Balloon/HEROES-Telescope	heroespy/notebooks/SAS PYAS-rear Analysis.ipynb	1	401653	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", ...	unlicense
WenmuZhou/cifar-10-cnn	3_Vgg19_Network/.ipynb_checkpoints/Vgg19_retrain_WHE_wd-checkpoint.ipynb	1	44628	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# use vgg19 to train the cifar-10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# import pakages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ ...	mit
Hash--/documents	notebooks/MDSplus/MDSplus_ToreSupra_example.ipynb	2	50881	{ "metadata": { "name": "", "signature": "sha256:afcba29030ce8626357a40262192489c1d8f719fbcd5965ad9dda7a81883c90f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Retrieving Tore Supra data...	mit
sebastianGehrmann/big-data-examples	.ipynb_checkpoints/GPM stats-checkpoint.ipynb	1	227236	{ "metadata": { "name": "", "signature": "sha256:a68f8fb821d0d305fbe02f9882cf84947cac78a200166ba53bcac7a99817475b" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# special IPython command to prepare the no...	mit
fabm-model/fabm	util/algorithms/running_max.ipynb	1	8032	{ "cells": [ { "cell_type": "markdown", "source": [ "# Prototype: an efficient, approximate running maximum filter\r\n", "\r\n", "The window size (units of time) is denoted as $p$.\r\n", "\r\n", "Constraints:\r\n", "* The filter uses a prescribed number of bins, $n$, to store the histor...	gpl-2.0
eds-uga/csci4360-fa17	workshops/w7/Workshop6_ Auto-Differentiation.ipynb	1	5624	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autograd" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time" ] }, { "cell_type": "markdown", "metadata": {}, ...	mit
DavidBrear/sklearn-cookbook	Chapter 2/2.3 Using Ridge Regression to Overcome Linear Regression Shortfalls.ipynb	1	87638	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Le...	apache-2.0
fdmazzone/Ecuaciones_Diferenciales	Teoria_Basica/Practicas/Practicas 2017/Practica+3.ipynb	1	52430	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sympy import *\n", "init_printing()\n", "t,x0,x1,x2,C=symbols('t,x0,x1,x2,C',real=true)" ] }, { "cell_type": "code", "execution_count": 2, "m...	gpl-2.0
JoshuaW1990/TuftsCOMP135_Spring2016	Lecture05/notebooks/instance_based_learning.ipynb	6	190555	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy\n", "import scipy.spatial\n", "\n", "...	apache-2.0
ioam/scipy-2017-holoviews-tutorial	notebooks/08-deploying-bokeh-apps.ipynb	1	12752	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a href='http://www.holoviews.org'><img src=\"assets/hv+bk.png\" alt=\"HV+BK logos\" width=\"40%;\" align=\"left\"/></a>\n", "<div style=\"float:right;\"><h2>08. Deploying Bokeh Apps</h2></div>" ] }, { "cell_type": "markd...	bsd-3-clause
chemiskyy/simmit	Examples/Umats/Damage/LLDM.ipynb	1	122343	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Composites simulation : plies with damage & plasticity" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\...	gpl-3.0
tanmay987/deepLearning	gan_mnist/Intro_to_GANs_Exercises.ipynb	1	25981	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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 ge...	mit
maxentile/advanced-ml-project	experiments/Random-autoencoder-ensembles.ipynb	1	493196	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import cPickle,gzip,os,sys,time\n", "dataset = 'mnist.pkl.gz'\n", "f = gzip.open(dataset, 'rb')\n", "train_set, val...	mit
zuphilip/ocropy	doc/line-normalization.ipynb	4	53131	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Line Normalization (dewarping)\n", "\n", "( These notes are based on: https://github.com/tmbdev/ocropy/blob/758e023f808d88e5995af54034c155621eb087b2/OLD/normalization-api.ipynb from 2014 )\n", "\n", "The line normaliz...	apache-2.0
tyler-skorczewski-math/tyler-skorczewski-math.github.io	covid19/covid-derivatives.ipynb	1	37919	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vc...	gpl-3.0
scitran/python-client	examples/fsl_bet.ipynb	1	4508	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found an existing token for this instance.\n", "The existing token has been refreshed.\n" ] } ...	gpl-3.0
ecervera/mindstorms-nb	nxt/index.ipynb	1	1918	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "[<img src=\"img/brick.jpg\" align=\"right\">](https://en.wikipedia.org/wiki/Lego_Mindstorms_NXT#NXT_Intelligent_Brick)\n", "# Mindstorms NXT\n", "Lego Mindstorms NXT és la segona generació de kits de ...	mit
pombredanne/https-gitlab.lrde.epita.fr-vcsn-vcsn	doc/notebooks/context.ladybird.ipynb	1	39773	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# `context`.`ladybird`(_`n`_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the Ladybird automaton with $n$ states. This family of automata is a worst case for determinization: its determiniz...	gpl-3.0

goedman/RobGoedmansNotebooks.jl

notebooks/SheehanOlver/02.ipynb

1

6699

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# What is a string?\n", "\n", "Strings can be created with quotation marks" ] }, { "cell_type": "code", "execution_count": 77, "metadata": { "collapsed": false }, "outputs": [ { "data": { ...

mit

opengridcc/opengrid

notebooks/Multi-variable Linear Regression Demo.ipynb

1

65888

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Multi-variable linear regression\n", "\n", "The multivariable linear regression analysis is used to create a model of a single variable, typically an energy consumption. We call this the *dependent* variable. The model is co...

apache-2.0

woobe/h2o_tutorials

introduction_to_machine_learning/py_04b_classification_ensembles.ipynb

1

26781

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning with H2O - Tutorial 4b: Classification Models (Ensembles)\n", "\n", "<hr>\n", "\n", "**Objective**:\n", "\n", "- This tutorial explains how to create stacked ensembles of classification models...

mit

GoogleCloudPlatform/asl-ml-immersion

notebooks/feature_engineering/solutions/5_tftransform_taxifare.ipynb

1

81069

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TfTransform # \n", "\n", "\n", "\n", "\n", "**Learning Objectives**\n", "1. Preproccess data and engineer new features using TfTransform \n", "1. Create and deploy Apache Beam pipeline \n", "1. Use pro...

apache-2.0

BoasWhip/Black

Notebook/M269 Unit 4 Notes -- Search.ipynb

1

36431

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 4. Searching" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 4.1 Searching Lists" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Algorithm: Selection\n", ...

mit

CompPhysics/ComputationalPhysics2

doc/Projects/2019/Project2CC/ipynb/Project2CC.ipynb

1

23306

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# Project 2, Coupled cluster studies of electronic systems. Deadline May 31\n", "<!-- dom:AUTHOR: [Computational Physics II FYS4...

cc0-1.0

hendrycks/fooling

MNIST/show_reconstructions.ipynb

1

37082

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Extracting MNIST_data/train-images-idx3-ubyte.gz\n", "Extracting MNIST_data/train-labels-idx1-ubyte.gz\n...

mit

aschaffn/phys202-2015-work

assignments/assignment02/ProjectEuler2.ipynb

1

2080

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 2" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "Each new term in the Fibonacci sequence is generated by adding the previous two ...

mit

CartoDB/cartoframes

docs/examples/data_visualization/layers/add_multiple_layers.ipynb

1

177463

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Add Multiple Layers\n", "\n", "In this example, three Layers are added to a Map. Notice the draw order and default symbology for each.\n", "\n", "For more information, run `help(Layer)`" ] }, { "cell_type":...

bsd-3-clause

kenjisato/intro-macro

doc/python/Optimal Growth (Euler).ipynb

1

85296

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Computing the Optimal Grwoth Model by the Euler Equation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "i...

mit

ccasotto/rmtk

rmtk/vulnerability/derivation_fragility/equivalent_linearization/lin_miranda_2008/lin_miranda_2008.ipynb

2

12031

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lin and Miranda (2008)\n", "\n", "This method, described in [Lin and Miranda (2008)](http://dx.doi.org/10.1061/%28ASCE%290733-9445%282008%29134:11%281685%29), estimates the maximum inelastic displacement of an existing struct...

agpl-3.0

GoogleCloudPlatform/asl-ml-immersion

notebooks/launching_into_ml/solutions/1_explore_data.ipynb

1

239477

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Explore and create ML datasets\n", "\n", "**Learning Objectives**\n", "* Access and explore a public BigQuery dataset on NYC Taxi Cab rides\n", "* Visualize your dataset using the Seaborn library\n", "* Inspect an...

apache-2.0

icoxfog417/tensorflow-arithmetic

tensorflow-arithmetic.ipynb

1

3287

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Arithmetic\n", "\n", "\n", "## calculation" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output...

mit

bpgc-cte/python2017

Week 2/Practice Set 1.ipynb

1

4638

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Practice Questions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", " 1. Take two input strings and print out every odd indexed character of first string and then\n", " followed ...

mit

fangohr/oommf-python

new/notebooks/fmr_standard_problem.ipynb

1

210213

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# FMR standard problem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Author**: Marijan Beg\n", "\n", "**Date**: 11 May 2016" ] }, { "cell_type": "markdown", "metadata": {}, ...

bsd-2-clause

frodo81/EnergyMonitor

emNotebook.ipynb

2

1662776

null

gpl-2.0

gangadhara691/gangadhara691.github.io

P3 wrangle_data/DataWrangling_ganga.ipynb

1

30449

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# **P3 - Data Wrangling with MongoDB**\n", "# **OpenStreetMap Project Data Wrangling with MongoDB**\n", "## Gangadhara Naga Sai<a name=\"top\"></a>\n", "Data used -<a href=https://mapzen.com/metro-extracts/> MapZen Weekly O...

mit

probml/pyprobml

notebooks/book1/10/iris_logreg.ipynb

1

316

{ "cells": [ { "cell_type": "markdown", "id": "75544f56", "metadata": {}, "source": [ "Source of this notebook is here: https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/02/iris_logreg.ipynb" ] } ], "metadata": {}, "nbformat": 4, "nbformat_minor": 5 }

mit

SumitDutta1997/Toy-Projects.

Tic_Tac_Toe.ipynb

1

5108

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# TIC TAC TOE\n", "\n", "from IPython.display import clear_output\n", "\n", "def display_board(board):\n", " clear_output()\n", " \n", ...

mit

hschilling/learning_ipython

complex_step_method-log_plot.ipynb

1

54041

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Taken from http://kitchingroup.cheme.cmu.edu/blog/2013/02/27/A-novel-way-to-numerically-estimate-the-derivative-of-a-function-comple...

mit

taliamo/Final_Project

organ_pitch/Scripts/.ipynb_checkpoints/upload_env_data-checkpoint.ipynb

1

91647

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## T. Martz-Oberlander, 2015-11-12, CO2 and Speed of Sound\n", "### Formatting ENVIRONMENTAL CONDITIONS pipe organ data for Python operations\n", "\n", "NOTE: Here, pitch and frequency are used interchangeably to signify th...

mit

datahac/jup

.ipynb_checkpoints/helper_weather_dairy-checkpoint.ipynb

1

20297

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Weather API connect" ] }, { "cell_type": "code", "execution_count": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAArMAAAIhCAYAAABdSTJTA...

apache-2.0

eds-uga/csci1360e-su16

lectures/L9.ipynb

1

25388

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Lecture 9: Introduction to Functions\n", "\n", "CSCI 1360E: Foundations for Informatics and Analytics" ] }, { "cell_type": "markdown", "metadata": { "sl...

mit

digitaldingo/task_force

Studiepraktikdag - dataanalyse.ipynb

1

101102

{ "metadata": { "name": "", "signature": "sha256:74d813854f2e100d25c35670aaefd23fe6210310678b27a0160dc53dc264d447" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline\n", "from ekstra_funkti...

mit

drvinceknight/gt

nbs/other/Schedule.ipynb

1

1460

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Approximate weekly schedule\n", "\n", "- Week 1:\n", " - About the course\n", " - Normal form games\n", "- Week 2:\n", " - Calculating utilities of strategies\n", " - Rationalisation\n", "-...

mit

rasbt/python-machine-learning-book

code/ch12/ch12.ipynb

1

1876868

null

mit

zhongdai/learn-scikit

ch02_svm_image_classifier.ipynb

1

2139958

null

mit

ES-DOC/esdoc-jupyterhub

notebooks/test-institute-3/cmip6/models/sandbox-1/land.ipynb

1

173522

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "**MIP Era**: CMIP6 \n", "**Institute**: TEST-INSTITUTE-3 \n", "**Source ID**: SANDBOX-1 \n", ...

gpl-3.0

jhartford/pybo

pybo - pygp tests.ipynb

1

16076

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pygp\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": false }, "outputs": [ ...

bsd-2-clause

idc9/law-net

vertex_metrics_experiment/chalboards/test_revised_pipeline.ipynb

1

5957

{ "cells": [ { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The autoreload extension is already loaded. To reload it, use:\n", " %reload_ext autoreload\n" ] ...

mit

CodeNeuro/notebooks

worker/notebooks/thunder/tutorials/factorization.ipynb

11

622198

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Factorization" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Factorization algorithms are useful for data-driven decomposition of spatial and temporal data, for example, to recover spatial patterns ...

mit

wheeler-microfluidics/teensy-minimal-rpc

teensy_minimal_rpc/notebooks/dma-examples/Example - [BROKEN] Periodic multi-channel ADC multiple samples using DMA and PIT.ipynb

1

32444

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# __NB__ Cannot use PIT to trigger periodic DMA due to hardware bug\n", "\n", "See [here][1].\n", "\n", "Try using PDB instead??\n", "\n", "[1]: https://community.freescale.com/thread/102320#112176" ] }, ...

gpl-3.0

antoniomezzacapo/qiskit-tutorial

community/games/game_engines/Making_your_own_hello_quantum.ipynb

1

306391

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Hello Quantum for Jupyter notebook\n", "\n", "[Hello Quantum](https://helloquantum.mybluemix.net/) is a project based on the idea of visualizing two qubit states and gates, and making them accessible to a non-specialist audie...

apache-2.0

agaveapi/SC17-container-tutorial

content/notebooks/Introduction.ipynb

1

14393

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# HPC via HTTP: Portable, Scalable Computing using App Containers and the Agave API" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Date:** Monday, Nov 13th \n", "**Time:** 8:30am - 12:00pm \n"...

bsd-3-clause

root-mirror/training

SummerStudentCourse/2019/Exercises/ROOTBooks/GausFit_Solution.ipynb

4

83811

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise: Gauss Histogram Fitting \n", "\n", "This example shows how to fit an histogram using ROOT " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create First an empty histogram with 50 b...

gpl-2.0

anonymous-ijcai/dsw-ont-ijcai

dswont/topics_music.ipynb

1

66610

{ "metadata": { "name": "", "signature": "sha256:4d8dd393c2552c4aa372b9b760eb57a8e00f9d67cac2562512edf8ecf8ce642f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# %matplotlib inline\n", "# %load_ext...

gpl-3.0

eco32i/biodata

sessions/templates/RNA-Seq/06 - PCA.ipynb

1

4999

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, "editable": true, "tags": [] }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "deletable": true, ...

mit

arcyfelix/Courses

17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/03-General Pandas/02-Series.ipynb

2

8521

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "___\n", "\n", "<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n", "___\n", "# Series" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first main dat...

apache-2.0

ramseylab/networkscompbio

class21_reveal_python3.ipynb

1

8356

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Class 21: joint entropy and the REVEAL algorithm" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll use the bladder cancer gene expression data to test out the REVEAL algorithm. First, we'll load...

apache-2.0

mne-tools/mne-tools.github.io

0.19/_downloads/4b44551162dc4f8dda6c7f0d2af501fe/plot_10_reading_meg_data.ipynb

1

13295

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

bsd-3-clause

elenduuche/deep-learning

seq2seq/sequence_to_sequence_implementation.ipynb

1

521478

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Character Sequence to Sequence \n", "In this notebook, we'll build a model that takes in a sequence of letters, and outputs a sorted version of that sequence. We'll do that using what we've learned so fa...

mit

rdempsey/web-scraping-data-mining-course

week8/2_bi_dashboards_reporting/4 - Create and Save the Plots for the Dashboard.ipynb

1

5433

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import the Python libraries we need\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib\n", "matplotlib.use('Agg')\n", "impor...

mit

phockett/ePSproc

notebooks/plottingDev/hvPlot_Holoviews_examples_140720.ipynb

1

2943775

null

gpl-3.0

jllanfranchi/pygeneric

pdfmerge.ipynb

1

6606

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import re\n", "import numpy as np\n", "from IPython.display import display as D\n", "%rehashx" ], "langua...

mit

lucduff/Recommend_Top_5_Thai_Restaurants_in_Las_Vegas

Recommender-Models/recommender_with_ratings.ipynb

1

44182182

null

mit

dream-olfaction/olfaction-prediction

opc_python/hulab/collaboration/scoring and graphs.ipynb

1

205908

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "compares the predicted results to the true results and calculates the scores \n", "creates the graph" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], ...

mit

swails/mdtraj

examples/principal-components.ipynb

6

4194

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "scikit-learn is a machine learning library for python, with a very easy to use API and great documentation." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": ...

lgpl-2.1

jan-rybizki/Galaxia_wrap

notebook/.ipynb_checkpoints/[4]mag_limited_survey_function-checkpoint.ipynb

1

18223

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "import os, sys\n", "path = os.path.abspath('../library/')\n", "if path not in sys.path:\n", " sys.path.append(path)\n", "fr...

mit

DamienIrving/ocean-analysis

development/EBAF-TOA.ipynb

1

607621

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## CERES TOA data\n", "\n", "Playing around with the CERES EBAF-TOA Ed4.0 net TOA all-sky radiative flux data downloaded from [here](https://ceres-tool.larc.nasa.gov/ord-tool/jsp/...

mit

marcinofulus/teaching

ML_SS2017/numpy_kto_szybciej.ipynb

1

42801

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

gpl-3.0

ajaybhat/DLND

Project 1/Project-1.ipynb

1

479418

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Your first neural network\n", "\n", "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural networ...

apache-2.0

EducationalTestingService/rsmtool

rsmtool/notebooks/summary/header.ipynb

1

7881

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Setting options for the plots\n", "%matplotlib inline\n", "%config InlineBackend.figure_formats={'retina', 'svg'}\n", "%config InlineBackend.rc={'savefig...

apache-2.0

ES-DOC/esdoc-jupyterhub

notebooks/pcmdi/cmip6/models/sandbox-3/land.ipynb

1

173500

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

gpl-3.0

janisz/bus-factor-calculator

marathon-consul/Calculate factor.ipynb

1

52720

{ "cells": [ { "cell_type": "code", "execution_count": 121, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import networkx as nx" ] }, { "cell_type": "code", "execution_count": 104, "metadata": { "collapsed": false }, "o...

mit

mne-tools/mne-tools.github.io

0.19/_downloads/a1ab4842a5aa341564b4fa0a6bf60065/plot_dipole_orientations.ipynb

1

13155

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

bsd-3-clause

kriukov/interval-methods

ipynb/henon_points.ipynb

2

37247

{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ ";cd .." ], "language": "python", "metadata": {}, "outputs": [ { "output_type"...

gpl-3.0

cavestruz/MLPipeline

notebooks/anomaly_detection/sample_anomaly_detection.ipynb

1

7074

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Let us first explore an example that falls under novelty detection. Here, we train a model on data with some distribution and no outliers. The test data, has some \"novel\" subset of data that does not follow that distribution." ]...

mit

mzszym/oedes

examples/scl/scl-trapping.ipynb

1

75101

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Steady-state space-charge-limited current with traps \n", "\n", "This example shows how to simulate effects of a single trap level on current-voltage characteristics of a single carrier device.\n", "\n" ] }, { "...

agpl-3.0

autumn-lake/TalkingData-Mobile-User-Demographics

ipynb_notebooks/brand_model_predictions_train-Copy1.ipynb

1

72432

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import xgboost as xgb\n", "%matplotlib inline\n", "#import seaborn as sns\n", "import matplotlib.pyp...

mit

bioinformatica-corso/2014-15

laboratorio/lezione18-17dic21/esercizio1-pysam.ipynb

3

4291

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Esercizio1 - Pysam" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prendere in input un BAM file che contiene allineamenti ad un'unica reference (cromosoma X) e:\n", "\n", "- controllare se s...

cc0-1.0

stereoboy/Study

papers/Attention_Models/Attention Models.ipynb

1

1475

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Hard Attention VS Soft Attention\n", "\n", "* Hard Attention\n", " * Chose Attention spots (or locations) using Stochastical methods\n", " * Use Reinforcement Learning\n", " * e.g. Re...

mit

ptpro3/ptpro3.github.io

Projects/Project2/Project2_Prashant.ipynb

1

950138

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "```\n", "Project: Project 2: Luther\n", "Date: 02/03/2017\n", "Name: Prashant Tatineni\n", "```" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Project Overview\n", "For ...

mit

SIMEXP/Projects

preventad/.ipynb_checkpoints/preventad_scores_dynamic-checkpoint.ipynb

1

317033

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Ok, quick outline:\n", "1. Have the network to left\n", "2. Have the network time series top center\n", "3. Take three time windows\n", "4. Have the seed map and winner takes all for each window in stack mid/bottom cent...

mit

PyLCARS/PythonUberHDL

myHDL_DigLogicFundamentals/myHDL_Combinational/Multiplexers(MUX).ipynb

1

312770

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\\title{myHDL Combinational Logic Elements: Multiplexers (MUXs))}\n", "\\author{Steven K Armour}\n", "\\maketitle" ] }, { "cell_type": "markdown", "metadata": { "toc": true }, "source": [ "<h1>Table o...

bsd-3-clause

dfm/emcee3

docs/user/parallel.ipynb

1

7701

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Parallelization\n", "===============\n", "\n", "**emcee** supports parallelization ...

mit

whitead/numerical_stats

unit_7/hw_2018/Homework_7_Key.ipynb

1

111431

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Homework 7 Key\n", "====\n", "#### CHE 116: Numerical Methods and Statistics\n", "\n", "3/8/2018\n", "\n", "----" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": ...

gpl-3.0

thewtex/SimpleITK-Notebooks

62_Registration_Tuning.ipynb

1

17093

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align=\"center\">Registration Settings: Choices, Choices, Choices</h1>\n", "\n", "The performance of most registration algorithms is dependent on a large number of parameter settings. For optimal performance you will need t...

apache-2.0

fmonti/mgcnn

Notebooks/douban/.ipynb_checkpoints/supervised_approach_douban_dataset_original_training_set_norm_acc_with_factorization_1_mono_conv_norm_W-checkpoint.ipynb

1

1585447

null

gpl-3.0

park-python/course

lectures/10_Python_Applications/pandas_tutorial.ipynb

1

43170

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Скачаем датасет матчей Испанской Ла Лиги за последние 90 лет\n",...

bsd-3-clause

alephcero/adsProject

olds/DataAnalysisPlanA.ipynb

1

82434

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# New York University\n", "## Applied Data Science 2016 Final Project\n", "----\n", "## Measuring household income under Redatam in CensusData\n", "\n", "**Project Description:** Lorem ipsum\n", "\n", "**Mem...

gpl-3.0

Rlee13/pyPLY

docs/examples/GraphiteEpoxy3LayersPolarPlot.ipynb

1

53312

{ "metadata": { "name": "GraphiteEpoxy3LayersPolarPlot" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "# Example of polar plot for GraphiteEpoxy (3x layers)\n", "# with dashed lines the theoretical quasi-i...

lgpl-2.1

m-walters/mcmd

rodplot.ipynb

1

435499

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import os\n", "import matplotlib.pyplot as plt\n", "import matplotlib.patches as pypatch\n", "import time\n", "%matplotlib inline\n", "\n", "cos...

mit

charlielito/supervised-basico-admission-exams

python-sample.ipynb

2

4493151

null

mit

talespaiva/folium

examples/Features.ipynb

1

337930

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# This is to import the repository's version of folium ; not the installed one.\n", "import sys, os\n", "sys.path.insert(0,'..')" ] }, { "cell_type": "c...

mit

bloomberg/bqplot

examples/Interactions/Interaction Layer.ipynb

2

25483

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "\n", "symbol = \"Security 1\"\n", "symbol2 = \"Security 2\"" ] }, { "cell_type": "code", "execution_count": null, ...

apache-2.0

crystalzhaizhai/cs207_yi_zhai

homeworks/HW6/HW6_finished.ipynb

1

64550

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework 6\n", "## Due: Tuesday, October 10 at 11:59 PM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Problem 1: Bank Account Revisited\n", "\n", "We are going to rewrite the bank ac...

mit

dm-wyncode/zipped-code

error_files/embrace_hug.ipynb

1

5583

{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import hug\n", "import webcolors" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": true }, "outputs": [], "...

mit

VUInformationRetrieval/IR2016_2017

04_analysis.ipynb

1

51342

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Mini-Assignment 4: Link Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this mini-assignment, we will exploit graph algorithms to improve search results. For our dataset of scientific pap...

gpl-2.0

shashank14/Asterix

1-Python Crash course/Python-Crash-Course/Python Crash Course Exercises .ipynb

1

12060

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python Crash Course Exercises \n", "\n", "This is an optional exercise to test your understanding of Python Basics. If you find this extremely challenging, then you probably are not ready for the rest of this course yet and d...

apache-2.0

chusine/dlnd

autoencoder/Convolutional_Autoencoder.ipynb

1

89430

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

mit

OSGeoLabBp/tutorials

english/data_processing/lessons/ml_clustering.ipynb

1

184051

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "ml_clustering.ipynb", "provenance": [], "collapsed_sections": [], "authorship_tag": "ABX9TyP1OxpHVNY5kooCRkzf55Us", "include_colab_link": true }, "kernelspec": { "name": "python3", "displa...

cc0-1.0

zsedem/haskell-playground

Notebooks/JiraActivtiesRSSpoc.ipynb

1

19978

{ "cells": [ { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [], "source": [ "{-# LANGUAGE OverloadedStrings, TemplateHaskell, DisambiguateRecordFields #-}\n", "import Data.Maybe\n", "import Control.Monad\n", "import Text.Feed.Import\n...

mit

HEROES-Balloon/HEROES-Telescope

heroespy/notebooks/SAS PYAS-rear Analysis.ipynb

1

401653

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", ...

unlicense

WenmuZhou/cifar-10-cnn

3_Vgg19_Network/.ipynb_checkpoints/Vgg19_retrain_WHE_wd-checkpoint.ipynb

1

44628

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# use vgg19 to train the cifar-10" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# import pakages" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ ...

mit

Hash--/documents

notebooks/MDSplus/MDSplus_ToreSupra_example.ipynb

2

50881

{ "metadata": { "name": "", "signature": "sha256:afcba29030ce8626357a40262192489c1d8f719fbcd5965ad9dda7a81883c90f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Retrieving Tore Supra data...

mit

sebastianGehrmann/big-data-examples

.ipynb_checkpoints/GPM stats-checkpoint.ipynb

1

227236

{ "metadata": { "name": "", "signature": "sha256:a68f8fb821d0d305fbe02f9882cf84947cac78a200166ba53bcac7a99817475b" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# special IPython command to prepare the no...

mit

fabm-model/fabm

util/algorithms/running_max.ipynb

1

8032

{ "cells": [ { "cell_type": "markdown", "source": [ "# Prototype: an efficient, approximate running maximum filter\r\n", "\r\n", "The window size (units of time) is denoted as $p$.\r\n", "\r\n", "Constraints:\r\n", "* The filter uses a prescribed number of bins, $n$, to store the histor...

gpl-2.0

eds-uga/csci4360-fa17

workshops/w7/Workshop6_ Auto-Differentiation.ipynb

1

5624

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Autograd" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import time" ] }, { "cell_type": "markdown", "metadata": {}, ...

mit

DavidBrear/sklearn-cookbook

Chapter 2/2.3 Using Ridge Regression to Overcome Linear Regression Shortfalls.ipynb

1

87638

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Le...

apache-2.0

fdmazzone/Ecuaciones_Diferenciales

Teoria_Basica/Practicas/Practicas 2017/Practica+3.ipynb

1

52430

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sympy import *\n", "init_printing()\n", "t,x0,x1,x2,C=symbols('t,x0,x1,x2,C',real=true)" ] }, { "cell_type": "code", "execution_count": 2, "m...

gpl-2.0

JoshuaW1990/TuftsCOMP135_Spring2016

Lecture05/notebooks/instance_based_learning.ipynb

6

190555

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

apache-2.0

ioam/scipy-2017-holoviews-tutorial

notebooks/08-deploying-bokeh-apps.ipynb

1

12752

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a href='http://www.holoviews.org'><img src=\"assets/hv+bk.png\" alt=\"HV+BK logos\" width=\"40%;\" align=\"left\"/></a>\n", "<div style=\"float:right;\"><h2>08. Deploying Bokeh Apps</h2></div>" ] }, { "cell_type": "markd...

bsd-3-clause

chemiskyy/simmit

Examples/Umats/Damage/LLDM.ipynb

1

122343

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Composites simulation : plies with damage & plasticity" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\...

gpl-3.0

tanmay987/deepLearning

gan_mnist/Intro_to_GANs_Exercises.ipynb

1

25981

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "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 ge...

mit

maxentile/advanced-ml-project

experiments/Random-autoencoder-ensembles.ipynb

1

493196

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import cPickle,gzip,os,sys,time\n", "dataset = 'mnist.pkl.gz'\n", "f = gzip.open(dataset, 'rb')\n", "train_set, val...

mit

zuphilip/ocropy

doc/line-normalization.ipynb

4

53131

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Line Normalization (dewarping)\n", "\n", "( These notes are based on: https://github.com/tmbdev/ocropy/blob/758e023f808d88e5995af54034c155621eb087b2/OLD/normalization-api.ipynb from 2014 )\n", "\n", "The line normaliz...

apache-2.0

tyler-skorczewski-math/tyler-skorczewski-math.github.io

covid19/covid-derivatives.ipynb

1

37919

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAD8CAYAAACcjGjIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vc...

gpl-3.0

scitran/python-client

examples/fsl_bet.ipynb

1

4508

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Found an existing token for this instance.\n", "The existing token has been refreshed.\n" ] } ...

gpl-3.0

ecervera/mindstorms-nb

nxt/index.ipynb

1

1918

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "[<img src=\"img/brick.jpg\" align=\"right\">](https://en.wikipedia.org/wiki/Lego_Mindstorms_NXT#NXT_Intelligent_Brick)\n", "# Mindstorms NXT\n", "Lego Mindstorms NXT és la segona generació de kits de ...

mit

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

doc/notebooks/context.ladybird.ipynb

1

39773

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# `context`.`ladybird`(_`n`_)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create the Ladybird automaton with $n$ states. This family of automata is a worst case for determinization: its determiniz...

gpl-3.0