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
omoju/Fundamentals	CS/Part_1_Complexity_RunTimeAnalysis.ipynb	1	62076	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}}...	gpl-3.0
YubinXie/Computational-Pathology	Prostate_Length.ipynb	2	708	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " The mpp_x and mpp_y is same in each slide." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "...	gpl-2.0
hbutler/InverseCCP	1 - Generate coupon probabilities - part 1.ipynb	1	11538	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import scipy.stats as stat\n", "from matplotlib import pyplot as plt" ] }, { "cell_type": "markdown"...	mit
aitatanit/metatlas	docs/example_notebooks/Prototype_Notebook_Using_IPython_NERSC_Interface.ipynb	1	6569	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import metatlas\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata":...	bsd-3-clause
chenleo/ipynotebook	Poisson.ipynb	1	51965	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "python", "metadata": {}, "outputs": [ { "output_type": "stream", ...	mit
YuriyGuts/kaggle-quora-question-pairs	notebooks/unused/feature-oofp-nn-lstm-with-activations.ipynb	1	16407	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Feature: Out-Of-Fold Predictions and Feature Layer Activations from an LSTM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the output of the final network layer, the model will also o...	mit
ioos/comt_notebooks	admin/Untitled0.ipynb	1	7050	{ "metadata": { "name": "", "signature": "sha256:6fd7d90de350752b942938eef2f88e4a1680d5aa8ae821f3b9f511ad2bfcd2e9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "plot(arange(10))" ], "language": ...	mit
queirozfcom/python-sandbox	python3/notebooks/subprocess-post/main.ipynb	2	23180	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:38.959003Z", "start_time": "2021-08-09T04:02:38.951775Z" } }, "outputs": [ { "data": { "text/plain": [ "'3.6.9'" ] }, "executio...	mit
exe0cdc/ipython-d3networkx	examples/demo simple.ipynb	5	2804	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.html import widgets\n", "from IPython.display import display\n", "from d3networkx import ForceDirectedGraph, EventfulGraph" ] }, { "cel...	mit
machow/siuba	docs/draft-old-pages/intro_sql_interm.ipynb	1	75470	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "nbsphinx": "hidden" }, "outputs": [], "source": [ "import matplotlib.cbook\n", "\n", "import warnings\n", "import plotnine\n", "warnings.filterwarnings(module='plotnine*', action='ignore')\n", "warn...	mit
wolf9s/doconce	doc/pub/admon/admon_quote.ipynb	2	4335	{ "metadata": {}, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demo of admonition styles in DocOnce\n", "May 2, 2015\n", "\n", "Summary. This note demonstrates how admonitions look l...	bsd-3-clause
jpilgram/phys202-2015-work	assignments/assignment10/ODEsEx03.ipynb	1	96234	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Ordinary Differential Equations Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "executio...	mit
madjelan/Data-Science-45min-Intros	adaboost/adaboost_tutorial.ipynb	2	1774341	null	unlicense
mne-tools/mne-tools.github.io	0.18/_downloads/7df5cd97aa959dd7e2627aba5e552081/plot_forward.ipynb	1	13976	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\nHead model a...	bsd-3-clause
igmhub/lyaforecast	examples/plot_camb_linPk.ipynb	1	36689	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Playing with CAMB" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs"...	gpl-3.0
magenta/magenta-demos	jupyter-notebooks/Sketch_RNN_TF_To_JS_Tutorial.ipynb	1	47281	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, I will show how to train the TensorFlow version of Sketch-RNN on a new dataset, and convert the weights of the TF model to a JSON format that is usable by Sketch-RNN-JS so that interactive web demos can be built.\n", ...	apache-2.0
lexieheinle/jour407homework	ChartSecondHomework/VisualizingPresidentialCommercials.ipynb	1	8506025	null	mit
emreyamangil/Convex.jl	examples/optimal_advertising.ipynb	6	652070	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "srand(1);\n", "using Distributions\n", "m = 5;\n", "n = 24;\n", "SCALE = 10000;\n", "B = rand(LogNormal(8), m) + 10000;\n", "B = round(B, 3);\n", ...	bsd-2-clause
outlace/Machine-Learning-Experiments	VariableOutput.ipynb	1	9212	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### A recursive neural network that decides how many times to run itself\n", "Produces variable-length outputs for static-length inputs." ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collap...	mit
jerkern/pyParticleEst	docs/example/BasicModel.ipynb	1	45516	{ "metadata": { "name": "", "signature": "sha256:14e723ea85ca9dc3dbcf17a8950cb60dfcd8fe1c7bc561fa16ccb902d9b504fd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Basic Model\n" ] ...	lgpl-3.0
Deltares/hydro-engine	notebooks/generate_index.ipynb	1	231702	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Generate spatial index for catchments based on their topology\n", "\n", "Save parent catchment info for every child catchment so that it can be quickly queried, O(n), eventually it is much faster with column value indices.\n", ...	lgpl-3.0
rjw57/vagrant-ipython	notebooks/matlab-example.ipynb	1	1067	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MATLAB integration\n", "\n", "This is a test of using MATLAB from the notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [...	mit
sueiras/training	tensorflow/00-basics/data_estimator_iris_example.ipynb	1	23092	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples of use tf.data and tf.estimator with the iris dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf...	gpl-3.0
kootsoop/DSP.SE	Python/SO70768384 Right method for finding 2-D Spatial Spectrum from cross spectral densities.ipynb	1	64961	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of CSD data (1156, 257)\n" ] }, { "data": { "text/plain": [ "Text(0.5, 1.0, 'Spatial Spectrum @10Hz'...	mit
tata-antares/tagging_LHCb	Stefania_files/track-based-tagging-experiments.ipynb	1	821806	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pyla...	apache-2.0
hktxt/MachineLearning	sorting.ipynb	1	8872	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### sorting" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "strings = [\"Beryl\", \"Magage\", \"Clafe\", \"Stfg\", \"Bargs\", \"Rafdg\"] # sort lsit" ] }, ...	gpl-3.0
machow/siuba	docs/backends.ipynb	1	41711	{ "cells": [ { "cell_type": "code", "execution_count": 16, "metadata": { "nbsphinx": "hidden" }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "pd.set_option(\"display.max_rows\", 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Back...	mit
MarekKZielinski/Predicting-Blood-Donations	.ipynb_checkpoints/Predict Blood Donations CNN-checkpoint.ipynb	1	16553550	null	mit
balarsen/pymc_learning	Distributions/TruncatedNormal.ipynb	1	321787	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TruncatedNormal\n", "how does this compare to a bounded variable?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "i...	bsd-3-clause
abnowack/PyTracer	scripts/tests/raytrace.ipynb	1	250347	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "%matplotlib notebook\n", "import matplotlib.pyplot as plt\n", "%load_ext Cython" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {...	mit
mne-tools/mne-tools.github.io	0.18/_downloads/c92aa91c680730c756234cdbc466c558/plot_introduction.ipynb	1	24435	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nOverview of ME...	bsd-3-clause
ProfessorKazarinoff/staticsite	content/code/webscrape/webscrape_html_table_with_pandas.ipynb	1	7004	{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import sys\n", "import urllib3,certifi\n", "import requests\n" ] }, { "cell_type": "code", "execution_count": 14, "m...	gpl-3.0
VandyAstroML/Vanderbilt_Computational_Bootcamp	notebooks/Week_05/05_Numpy_Matplotlib.ipynb	1	92420	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Week 5 - Numpy & Matplotlib\n", "\n", "## Today's Agenda\n", "* Numpy\n", "* Matplotlib" ] }, { "cell_type": "markdown", "metadata": { "deletable": true...	mit
google/earthengine-api	python/examples/ipynb/UNET_regression_demo.ipynb	1	31375	{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNET_regression_demo.ipynb","provenance":[{"file_id":"https://github.com/google/earthengine-api/blob/master/python/examples/ipynb/UNET_regression_demo.ipynb","timestamp":1586992475463}],"private_outputs":true,"collapsed_sections":[],"toc_visible":true,"machi...	apache-2.0
Cyb3rWard0g/HELK	docker/helk-jupyter/notebooks/sigma/win_susp_powershell_empire_launch.ipynb	1	3455	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Empire PowerShell Launch Parameters\n", "Detects suspicious powershell command line parameters used in Empire" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", ...	gpl-3.0
TomAugspurger/pyowa	flights.ipynb	1	2604828	null	mit
ES-DOC/esdoc-jupyterhub	notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb	1	209021	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Atmos \n", "MIP Era: CMIP6 \n", "Institute: TEST-INSTITUTE-2 \n", "Source ID: SANDBOX-3 \n", ...	gpl-3.0
winpython/winpython_afterdoc	docs/installing_julia_and_ijulia.ipynb	2	13034	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Installating Julia/IJulia \n", "\n", "### 1 - Downloading and Installing the right Julia binary in the right place" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "so...	mit
hakonsbm/nest-simulator	doc/topology/examples/grid_iaf.ipynb	1	1796	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nNEST Topology ...	gpl-2.0
mathnathan/notebooks	mpfi/probability blog post.ipynb	1	124466	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...	mit
ESMG/ESMG-configs	CCS1/preprocessing/CreateFMSgridTopo.ipynb	1	156388	{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy\n", "import scipy.io\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 11...	gpl-3.0
ES-DOC/esdoc-jupyterhub	notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb	1	99839	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "MIP Era: CMIP6 \n", "Institute: EC-EARTH-CONSORTIUM \n", "Source ID: EC-EARTH3-CC \n", ...	gpl-3.0
greenca/diy-spectrometer	peak-detection.ipynb	1	75028	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Detecting Peaks in a Spectrum" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ...	mit
tanghaibao/goatools	notebooks/cell_cycle.ipynb	1	115266	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cell Cycle genes\n", "Using Gene Ontologies (GO), create an up-to-date list of all human protein-coding genes that are know to be associated with cell cycle." ] }, { "cell_type": "markdown", "metadata": {}, "sourc...	bsd-2-clause
AkshanshChahal/BTP	Baseline 2.ipynb	1	18442	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Establishing a Baseline for the Problem\n", "## Using variety of regression algorithms (non linear)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import p...	mit
rbiswas4/Cadence	LSSTmetrics/readingLightCurves.ipynb	1	185222	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sncosmo\n", "import analyzeSN as ans\n", "from analyzeSN import LightCurve" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {...	mit
HWNi/data-512-a1	hcds-a1-data-curation.ipynb	2	173304	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A1 Data Curation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step1: Data Acquisition" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }...	mit
gregcaporaso/Extensible-Evaluation-Framework-Presentation	1-evaluation-framework-results.ipynb	1	129671	{ "metadata": { "name": "1-evaluation-framework-results" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Taxonomic assignment method evaluation framework\n", "==========================================...	bsd-3-clause
luwei0917/awsemmd_script	notebook/Optimization/hybrid_simulation_analysis_dec07.ipynb	1	9580790	null	mit
mayankjohri/LetsExplorePython	Section 2 - Advance Python/Chapter S2.05 - REST API - Server & Clients/requests.ipynb	2	9881	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "endpoint = 'https://raw.githubuse...	gpl-3.0
giraph/data-sci	hobart-temp/coffs-harbor-temp.ipynb	1	827314	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coffs Harbor temperature\n", "7/28/2017 coffs-harbor-temp.ipynb\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up" ] }, { "cell_type": "code", "execution_count": 1, "m...	unlicense
yingjun2/project-spring2017	part2/bin/FP+Q2+Xiaoliang+-+v1.0.ipynb	1	28036	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 96, "metadata": ...	bsd-3-clause
baumanab/noaa_requests	NOAA_sandbox.ipynb	1	26222	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from pandas.io import json\n", "import requests\n", "import...	gpl-3.0
peterwittek/ipython-notebooks	Parameteric and Bilevel Polynomial Optimization Problems.ipynb	1	77812	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Relaxations of parametric and bilevel polynomial optimization problems\n", "========================================\n", "Suppose we are interested in finding the global optimum of the following constrained polynomial optimizat...	gpl-3.0
nilbody/h2o-3	h2o-docs/src/booklets/v2_2015/source/DeepLearning_Vignette.ipynb	15	4853	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#---------------------------------------------------------------------\n", "#\n", "# Include and run all the Python code snippets from the H2O Deep Learning Vi...	apache-2.0
izaid/dynd-python	docs/notebooks/SciPy2013Intro.ipynb	8	16177	{ "metadata": { "name": "", "signature": "sha256:59d5d6f4be6a184d13faea3e888d526f35876a3a49d4feb01c62a7e6e4682589" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sys, dynd\n", "print('Python %...	bsd-2-clause
Vizzuality/gfw	docs/Update_GFW_Layers_Vault.ipynb	2	95954	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Create Layer Config Backup\n", "\n", "This notebook outlines how to run a process to create a remote backup of gfw layers.\n", "\n", "Rough process:\n", "\n", "- Run this notebook from the `gfw/data` folder\n"...	mit
pastas/pasta	examples/notebooks/02_fix_parameters.ipynb	1	287265	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time Series Analysis with Pastas\n", " \n", "Developed by Mark Bakker, TU Delft" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Required files to run this notebook (all available from th...	mit
Sz593/coursera_ml_notes	Jupyter Notebooks/LaTeX Notes Sandboxes/Coursera ML Notes Tex 2 - Logistic Regression.ipynb	1	350317	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "import datetime as dt\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from scipy import stats, constant...	mit
kimkipyo/dss_git_kkp	Python 복습/07일차.목_크롤링, 정규표현식/7일차_4T_크롤링(직방_json_api).ipynb	1	675787	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "ou...	mit
UCSBarchlab/PyRTL	ipynb-examples/introduction-to-hardware.ipynb	1	10106	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Hardware Design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This code works through the hardware design process with the the\n", "audience of software developers more in mind....	bsd-3-clause
DavidMcDonald1993/ghsom	parameter_tests_density.ipynb	2	148859	{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading nmi score progress\n", "loading running time progress\n", "\n", ...	gpl-2.0
amygdala/tensorflow-workshop	workshop_sections/high_level_APIs/mnist_eager_keras-debug.ipynb	1	14437	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Keras with TensorFlow eager mode, on the 'Fashion MNIST' dataset\n", "\n", "In this notebook, we'll use [TensorFlow's new eager execution mode](https://www.tensorflow.org/programmers_guide/eager)." ] }, { "cel...	apache-2.0
LucaCanali/Miscellaneous	Pyspark_SQL_Magic_Jupyter/IPython_Pyspark_SQL_Magic.ipynb	1	14926	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IPython magic functions for Pyspark\n", "# Examples of shortcuts for executing SQL in Spark" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "sourc...	apache-2.0
aitatanit/metatlas	4notebooks/old/examplenotebooks/MetAtlas_005_Find_Data_in_Grouped_Files_For_Given_Atlas.ipynb	1	3608986	null	bsd-3-clause
adrn/TwoFace	notebooks/figures/HighK-multimodal.ipynb	1	16764	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from os import path\n", "\n", "# Third-party\n", "from astropy.io import fits\n", "from astropy.stats import median_absolute_deviation\n", "from astropy.t...	mit
pramitchoudhary/Experiments	notebook_gallery/other_experiments/build-models/model-selection-and-tuning/current-solutions/TPOT/TPOT-demo.ipynb	1	15275	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"1000\"\n", " height=\"1000\"\n", ...	unlicense
camigord/Self-Driving-Car-Nanodegree	Camera_Calibration/camera_calibration.ipynb	1	1691676	null	mit
efoley/deep-learning	transfer-learning/Transfer_Learning.ipynb	3	23475	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Transfer Learning\n", "\n", "Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks ...	mit
Am3ra/CS	CS4/Metodos/Generación de números aleatorios.ipynb	1	1174	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Alan Macedo Esparza\n", "A01366288" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4 3 2 1 2 1 1 2...	mit
giacomov/astromodels	examples/EBL_attenuation_example.ipynb	2	35446	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EBL Attenuation of a Spectral Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we create a simple astromodels spectrum and then apply EBL attenuation, as a function of redshif...	bsd-3-clause
parrt/msan501	notes/sound.ipynb	1	2087627	null	mit
jmhsi/justin_tinker	data_science/courses/cs231/simple_net_spiral_data.ipynb	1	6985	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "N = 100 # number of points per class\n", "D = 2 # dimensionality\n", "K = 3 # number of classes\n", "X = np.zeros((N*K,D)) # data matrix (each row = single example)\n", "y = np....	apache-2.0
Myllyenko/incubator-toree	etc/examples/notebooks/meetup-streaming-toree.ipynb	8	29770	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Streaming Meetups Dashboard" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The purpose of this notebook is to give an all-in-one demo of streaming data from the [meetup.com RSVP API](http://www.meet...	apache-2.0
IgorWang/MachineLearningPracticer	basic/Tree-Based Methods.ipynb	2	2650485	null	gpl-3.0
shngli/Data-Mining-Python	Google Scholar network/google scholar.ipynb	1	938395	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Google Scholar Visualization\n", "Credit: chengjun's scholarNetwork script " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize Justin Wolfers' Google Scholar network" ] }, { ...	gpl-3.0
gklambauer/SelfNormalizingNetworks	SelfNormalizingNetworks_CNN_MNIST.ipynb	1	56613	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Tutorial on self-normalizing networks on the MNIST data set: convolutional neural networks\n", "\n", "Author: Guenter Klambauer, 2017\n", "\n", "tested under Python 3.5 and Tensorflow 1.1 ...	gpl-3.0
PhilHarnish/forge	src/puzzle/examples/mscpc/yr2016/journal_logs.ipynb	1	1708	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import forge\n", "from puzzle.puzzlepedia import puzzlepedia\n", "\n", "puzzle_dates = puzzlepedia.parse(\"\"\"\n", "01110 01111 00001 01001 10010\n", ...	mit
ZwickyTransientFacility/ztf_sim	notebooks/analyze_sim.ipynb	1	294354	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "import os\n", "import pandas as pd\n", "import numpy as np\n", "%matplotlib inline" ...	bsd-3-clause
royalosyin/Python-Practical-Application-on-Climate-Variability-Studies	ex15-Trend and Anomaly Analyses of Long-term Tempro-Spatial Dataset.ipynb	1	295878	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", "\n", ".rendered_html {\n", " font-family: \"proxima-nova\", helvetica;\n", " font-size: 130%;\n", " line-height...	mit
spacedrabbit/PythonBootcamp	Advanced Modules/Collections Module.ipynb	1	14356	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { ...	mit
percyfal/bokeh	examples/howto/Range update callback.ipynb	1	3562	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "\n", "import numpy as np\n", "\n", "from bokeh.models import ColumnDataSource, CustomJS, Rect\n", "from bokeh...	bsd-3-clause
tensorflow/docs-l10n	site/en-snapshot/io/tutorials/orc.ipynb	2	9413	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2021 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "id":...	apache-2.0
ledeprogram/algorithms	class5/Simple_Linear_Regression.ipynb	1	73478	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)\n", "import stat...	gpl-3.0
ray-project/ray	doc/source/ray-contribute/docs.ipynb	1	20550	{ "cells": [ { "cell_type": "markdown", "id": "8c7dab40", "metadata": {}, "source": [ "(docs-contribute)=\n", "\n", "# Contributing to the Ray Documentation\n", "\n", "There are many ways to contribute to the Ray documentation, and we're always looking for new contributors.\n", "E...	apache-2.0
phoebe-project/phoebe2-docs	2.2/tutorials/requiv_crit_detached.ipynb	1	6075	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Critical Radii: Detached Systems\n", "============================\n", "\n", "Setup\n", "-----------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first make s...	gpl-3.0
hide-tono/python-training	python-system-trade/sandbox/point_and_figure.ipynb	1	70519	{ "cells": [ { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "\n", "import matplotlib.finance as mpf\n", "import matplotlib.pyplot as plt\n", "import pandas\n", "from matplotlib.dates import d...	apache-2.0
jesserobertson/pynoddy	docs/notebooks/Sensitivity-Analysis.ipynb	1	255933	{ "metadata": { "name": "", "signature": "sha256:bfe0404ce8becc0369bb874843c773476bb9b85480c3ba4ef61b1c3a5bf5d5e3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sensitivity Analysis\n", "\n", ...	gpl-2.0
ucsc-astro/coffee	15_01_27_sqlalchemy/Examples.ipynb	1	116323	{ "metadata": { "name": "", "signature": "sha256:bdd723e0f126edb12e17b34d6123c51711e39d0daf416f1f0f7e431032ab2f2a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Learning SQLAlchemy\n", "\n", ...	gpl-3.0
ganguli-lab/single-trial	notebooks/Analysis - GP Manifold Recovery.ipynb	1	143490	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Relevant library" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING...	gpl-2.0
DavidPowell/OpenModes	test/Test MFIE.ipynb	1	4555	{ "metadata": { "name": "", "signature": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import os.path as osp\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", ...	gpl-3.0
tiagogiraldo/Machine_Learning_Nanodegree_Projects	student_intervention - 4.ipynb	1	46701	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Supervised Learning\n", "## Project 2: Building a Student Intervention System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Welcome to the secon...	gpl-3.0
dipanjank/ml	text_classification_and_clustering/step_3_classification_of_full_dataset.ipynb	1	244435	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align=\"center\">Level and Group Classification on Train and Test Datasets</h1>\n", "\n", "We have two classification tasks:\n", "\n", "* Predict the level, which ranges from 1-16.\n", "* Predict the group of a ...	gpl-3.0
hypergravity/cham_hates_python	exercise/cham_teaches_python_05_aplpy_healpy.ipynb	2	222287	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# APLpy basics\n", "\n", "[aplpyreaddocs](http://aplpy.readthedocs.io/en/stable/#)\n", "\n", "### - Introduction\n", "\n", " APLpy : Astronomical Plotting Library in Python.\n", "\n", " For conveni...	mit
fabianrost84/Rost-Rodrigo-Albors-et-al-2016	calculations/outgrowth.ipynb	1	265671	{ "cells": [ { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import scipy as sp\n", "from uncertainties import ufloat\n", "%matplotlib inline\n", ...	bsd-3-clause
mauriciogtec/PropedeuticoDataScience2017	Alumnos/Fernando_Briseno/.ipynb_checkpoints/Tarea_2_Fernando_Briseno-checkpoint.ipynb	1	3604695	null	mit
mtasende/Machine-Learning-Nanodegree-Capstone	notebooks/prod/n08_simple_q_learner_fast_learner_full_training.ipynb	1	271897	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# In this notebook a simple Q learner will be trained and evaluated. The Q learner recommends when to buy or sell shares of one particular stock, and in which quantity (in fact it determines the desired fracti...	mit
cpatrickalves/simprev	notebooks/CalculoEstoqueMetodoProb.ipynb	1	62576	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sugestão de metodologia para cálculo de Intervalos de Confiança \n", "\n", "Conforme mencionado na LDO de 2018, o modelo oficial do governo se define como determinístico: \n", "\n", "“[...] *ou seja, a partir da fixaç...	gpl-3.0
Z0m6ie/Zombie_Code	Data_Science_Course/Michigan Data Analysis Course/0 Introduction to Data Science in Python/Week4/Week+4.ipynb	1	52061	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "_You are currently looking at version 1.0 of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www...	mit
koverholt/notebooks	dask/create-cluster.ipynb	1	2533	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a Dask cluster using Coiled" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we'll create a Dask cluster with Coiled:" ] }, { "cell_type": "code", "execution_count": 1, ...	bsd-3-clause

omoju/Fundamentals

CS/Part_1_Complexity_RunTimeAnalysis.ipynb

1

62076

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<script>jQuery(function() {if (jQuery(\"body.notebook_app\").length == 0) { jQuery(\".input_area\").toggle(); jQuery(\".prompt\").toggle();}}...

gpl-3.0

YubinXie/Computational-Pathology

Prostate_Length.ipynb

2

708

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " The mpp_x and mpp_y is same in each slide." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "...

gpl-2.0

hbutler/InverseCCP

1 - Generate coupon probabilities - part 1.ipynb

1

11538

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

mit

aitatanit/metatlas

docs/example_notebooks/Prototype_Notebook_Using_IPython_NERSC_Interface.ipynb

1

6569

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

bsd-3-clause

chenleo/ipynotebook

Poisson.ipynb

1

51965

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

mit

YuriyGuts/kaggle-quora-question-pairs

notebooks/unused/feature-oofp-nn-lstm-with-activations.ipynb

1

16407

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Feature: Out-Of-Fold Predictions and Feature Layer Activations from an LSTM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to the output of the final network layer, the model will also o...

mit

ioos/comt_notebooks

admin/Untitled0.ipynb

1

7050

{ "metadata": { "name": "", "signature": "sha256:6fd7d90de350752b942938eef2f88e4a1680d5aa8ae821f3b9f511ad2bfcd2e9" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "plot(arange(10))" ], "language": ...

mit

queirozfcom/python-sandbox

python3/notebooks/subprocess-post/main.ipynb

2

23180

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2021-08-09T04:02:38.959003Z", "start_time": "2021-08-09T04:02:38.951775Z" } }, "outputs": [ { "data": { "text/plain": [ "'3.6.9'" ] }, "executio...

mit

exe0cdc/ipython-d3networkx

examples/demo simple.ipynb

5

2804

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from IPython.html import widgets\n", "from IPython.display import display\n", "from d3networkx import ForceDirectedGraph, EventfulGraph" ] }, { "cel...

mit

machow/siuba

docs/draft-old-pages/intro_sql_interm.ipynb

1

75470

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "nbsphinx": "hidden" }, "outputs": [], "source": [ "import matplotlib.cbook\n", "\n", "import warnings\n", "import plotnine\n", "warnings.filterwarnings(module='plotnine*', action='ignore')\n", "warn...

mit

wolf9s/doconce

doc/pub/admon/admon_quote.ipynb

2

4335

{ "metadata": {}, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Demo of admonition styles in DocOnce\n", "**May 2, 2015**\n", "\n", "**Summary.** This note demonstrates how admonitions look l...

bsd-3-clause

jpilgram/phys202-2015-work

assignments/assignment10/ODEsEx03.ipynb

1

96234

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Ordinary Differential Equations Exercise 3" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "executio...

mit

madjelan/Data-Science-45min-Intros

adaboost/adaboost_tutorial.ipynb

2

1774341

null

unlicense

mne-tools/mne-tools.github.io

0.18/_downloads/7df5cd97aa959dd7e2627aba5e552081/plot_forward.ipynb

1

13976

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

bsd-3-clause

igmhub/lyaforecast

examples/plot_camb_linPk.ipynb

1

36689

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Playing with CAMB" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs"...

gpl-3.0

magenta/magenta-demos

jupyter-notebooks/Sketch_RNN_TF_To_JS_Tutorial.ipynb

1

47281

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook, I will show how to train the TensorFlow version of Sketch-RNN on a new dataset, and convert the weights of the TF model to a JSON format that is usable by Sketch-RNN-JS so that interactive web demos can be built.\n", ...

apache-2.0

lexieheinle/jour407homework

ChartSecondHomework/VisualizingPresidentialCommercials.ipynb

1

8506025

null

mit

emreyamangil/Convex.jl

examples/optimal_advertising.ipynb

6

652070

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "srand(1);\n", "using Distributions\n", "m = 5;\n", "n = 24;\n", "SCALE = 10000;\n", "B = rand(LogNormal(8), m) + 10000;\n", "B = round(B, 3);\n", ...

bsd-2-clause

outlace/Machine-Learning-Experiments

VariableOutput.ipynb

1

9212

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### A recursive neural network that decides how many times to run itself\n", "Produces variable-length outputs for static-length inputs." ] }, { "cell_type": "code", "execution_count": 118, "metadata": { "collap...

mit

jerkern/pyParticleEst

docs/example/BasicModel.ipynb

1

45516

{ "metadata": { "name": "", "signature": "sha256:14e723ea85ca9dc3dbcf17a8950cb60dfcd8fe1c7bc561fa16ccb902d9b504fd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Basic Model\n" ] ...

lgpl-3.0

Deltares/hydro-engine

notebooks/generate_index.ipynb

1

231702

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Generate spatial index for catchments based on their topology\n", "\n", "Save parent catchment info for every child catchment so that it can be quickly queried, O(n), eventually it is much faster with column value indices.\n", ...

lgpl-3.0

rjw57/vagrant-ipython

notebooks/matlab-example.ipynb

1

1067

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MATLAB integration\n", "\n", "This is a test of using MATLAB from the notebook." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [...

mit

sueiras/training

tensorflow/00-basics/data_estimator_iris_example.ipynb

1

23092

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples of use tf.data and tf.estimator with the iris dataset" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import tensorflow as tf...

gpl-3.0

kootsoop/DSP.SE

Python/SO70768384 Right method for finding 2-D Spatial Spectrum from cross spectral densities.ipynb

1

64961

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Shape of CSD data (1156, 257)\n" ] }, { "data": { "text/plain": [ "Text(0.5, 1.0, 'Spatial Spectrum @10Hz'...

mit

tata-antares/tagging_LHCb

Stefania_files/track-based-tagging-experiments.ipynb

1

821806

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace from numpy and matplotlib\n" ] } ], "source": [ "%pyla...

apache-2.0

hktxt/MachineLearning

sorting.ipynb

1

8872

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### sorting" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "strings = [\"Beryl\", \"Magage\", \"Clafe\", \"Stfg\", \"Bargs\", \"Rafdg\"] # sort lsit" ] }, ...

gpl-3.0

machow/siuba

docs/backends.ipynb

1

41711

{ "cells": [ { "cell_type": "code", "execution_count": 16, "metadata": { "nbsphinx": "hidden" }, "outputs": [], "source": [ "import pandas as pd\n", "\n", "pd.set_option(\"display.max_rows\", 5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Back...

mit

MarekKZielinski/Predicting-Blood-Donations

.ipynb_checkpoints/Predict Blood Donations CNN-checkpoint.ipynb

1

16553550

null

mit

balarsen/pymc_learning

Distributions/TruncatedNormal.ipynb

1

321787

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TruncatedNormal\n", "how does this compare to a bounded variable?" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "i...

bsd-3-clause

abnowack/PyTracer

scripts/tests/raytrace.ipynb

1

250347

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

mit

mne-tools/mne-tools.github.io

0.18/_downloads/c92aa91c680730c756234cdbc466c558/plot_introduction.ipynb

1

24435

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

bsd-3-clause

ProfessorKazarinoff/staticsite

content/code/webscrape/webscrape_html_table_with_pandas.ipynb

1

7004

{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import sys\n", "import urllib3,certifi\n", "import requests\n" ] }, { "cell_type": "code", "execution_count": 14, "m...

gpl-3.0

VandyAstroML/Vanderbilt_Computational_Bootcamp

notebooks/Week_05/05_Numpy_Matplotlib.ipynb

1

92420

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Week 5 - Numpy & Matplotlib\n", "\n", "## Today's Agenda\n", "* Numpy\n", "* Matplotlib" ] }, { "cell_type": "markdown", "metadata": { "deletable": true...

mit

google/earthengine-api

python/examples/ipynb/UNET_regression_demo.ipynb

1

31375

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"UNET_regression_demo.ipynb","provenance":[{"file_id":"https://github.com/google/earthengine-api/blob/master/python/examples/ipynb/UNET_regression_demo.ipynb","timestamp":1586992475463}],"private_outputs":true,"collapsed_sections":[],"toc_visible":true,"machi...

apache-2.0

Cyb3rWard0g/HELK

docker/helk-jupyter/notebooks/sigma/win_susp_powershell_empire_launch.ipynb

1

3455

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Empire PowerShell Launch Parameters\n", "Detects suspicious powershell command line parameters used in Empire" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Rule Content\n", "```\n", ...

gpl-3.0

TomAugspurger/pyowa

flights.ipynb

1

2604828

null

mit

ES-DOC/esdoc-jupyterhub

notebooks/test-institute-2/cmip6/models/sandbox-3/atmos.ipynb

1

209021

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Atmos \n", "**MIP Era**: CMIP6 \n", "**Institute**: TEST-INSTITUTE-2 \n", "**Source ID**: SANDBOX-3 \n", ...

gpl-3.0

winpython/winpython_afterdoc

docs/installing_julia_and_ijulia.ipynb

2

13034

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Installating Julia/IJulia \n", "\n", "### 1 - Downloading and Installing the right Julia binary in the right place" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "so...

mit

hakonsbm/nest-simulator

doc/topology/examples/grid_iaf.ipynb

1

1796

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

gpl-2.0

mathnathan/notebooks

mpfi/probability blog post.ipynb

1

124466

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

mit

ESMG/ESMG-configs

CCS1/preprocessing/CreateFMSgridTopo.ipynb

1

156388

{ "cells": [ { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy\n", "import scipy.io\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 11...

gpl-3.0

ES-DOC/esdoc-jupyterhub

notebooks/ec-earth-consortium/cmip6/models/ec-earth3-cc/seaice.ipynb

1

99839

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "**MIP Era**: CMIP6 \n", "**Institute**: EC-EARTH-CONSORTIUM \n", "**Source ID**: EC-EARTH3-CC \n", ...

gpl-3.0

greenca/diy-spectrometer

peak-detection.ipynb

1

75028

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Detecting Peaks in a Spectrum" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ...

mit

tanghaibao/goatools

notebooks/cell_cycle.ipynb

1

115266

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Cell Cycle genes\n", "Using Gene Ontologies (GO), create an up-to-date list of all human protein-coding genes that are know to be associated with cell cycle." ] }, { "cell_type": "markdown", "metadata": {}, "sourc...

bsd-2-clause

AkshanshChahal/BTP

Baseline 2.ipynb

1

18442

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Establishing a Baseline for the Problem\n", "## Using variety of regression algorithms (non linear)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import p...

mit

rbiswas4/Cadence

LSSTmetrics/readingLightCurves.ipynb

1

185222

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sncosmo\n", "import analyzeSN as ans\n", "from analyzeSN import LightCurve" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {...

mit

HWNi/data-512-a1

hcds-a1-data-curation.ipynb

2

173304

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# A1 Data Curation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Step1: Data Acquisition" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }...

mit

gregcaporaso/Extensible-Evaluation-Framework-Presentation

1-evaluation-framework-results.ipynb

1

129671

{ "metadata": { "name": "1-evaluation-framework-results" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Taxonomic assignment method evaluation framework\n", "==========================================...

bsd-3-clause

luwei0917/awsemmd_script

notebook/Optimization/hybrid_simulation_analysis_dec07.ipynb

1

9580790

null

mit

mayankjohri/LetsExplorePython

Section 2 - Advance Python/Chapter S2.05 - REST API - Server & Clients/requests.ipynb

2

9881

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "endpoint = 'https://raw.githubuse...

gpl-3.0

giraph/data-sci

hobart-temp/coffs-harbor-temp.ipynb

1

827314

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coffs Harbor temperature\n", "7/28/2017 *coffs-harbor-temp.ipynb*\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set up" ] }, { "cell_type": "code", "execution_count": 1, "m...

unlicense

yingjun2/project-spring2017

part2/bin/FP+Q2+Xiaoliang+-+v1.0.ipynb

1

28036

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", "execution_count": 96, "metadata": ...

bsd-3-clause

baumanab/noaa_requests

NOAA_sandbox.ipynb

1

26222

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false, "deletable": true, "editable": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "from pandas.io import json\n", "import requests\n", "import...

gpl-3.0

peterwittek/ipython-notebooks

Parameteric and Bilevel Polynomial Optimization Problems.ipynb

1

77812

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Relaxations of parametric and bilevel polynomial optimization problems\n", "========================================\n", "Suppose we are interested in finding the global optimum of the following constrained polynomial optimizat...

gpl-3.0

nilbody/h2o-3

h2o-docs/src/booklets/v2_2015/source/DeepLearning_Vignette.ipynb

15

4853

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#---------------------------------------------------------------------\n", "#\n", "# Include and run all the Python code snippets from the H2O Deep Learning Vi...

apache-2.0

izaid/dynd-python

docs/notebooks/SciPy2013Intro.ipynb

8

16177

{ "metadata": { "name": "", "signature": "sha256:59d5d6f4be6a184d13faea3e888d526f35876a3a49d4feb01c62a7e6e4682589" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import sys, dynd\n", "print('Python %...

bsd-2-clause

Vizzuality/gfw

docs/Update_GFW_Layers_Vault.ipynb

2

95954

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Create Layer Config Backup\n", "\n", "This notebook outlines how to run a process to create a remote backup of gfw layers.\n", "\n", "Rough process:\n", "\n", "- Run this notebook from the `gfw/data` folder\n"...

mit

pastas/pasta

examples/notebooks/02_fix_parameters.ipynb

1

287265

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Time Series Analysis with Pastas\n", " \n", "*Developed by Mark Bakker, TU Delft*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Required files to run this notebook (all available from th...

mit

Sz593/coursera_ml_notes

Jupyter Notebooks/LaTeX Notes Sandboxes/Coursera ML Notes Tex 2 - Logistic Regression.ipynb

1

350317

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os\n", "import sys\n", "import datetime as dt\n", "\n", "import numpy as np\n", "import pandas as pd\n", "from scipy import stats, constant...

mit

kimkipyo/dss_git_kkp

Python 복습/07일차.목_크롤링, 정규표현식/7일차_4T_크롤링(직방_json_api).ipynb

1

675787

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests\n", "from bs4 import BeautifulSoup" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "ou...

mit

UCSBarchlab/PyRTL

ipynb-examples/introduction-to-hardware.ipynb

1

10106

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Hardware Design" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This code works through the hardware design process with the the\n", "audience of software developers more in mind....

bsd-3-clause

DavidMcDonald1993/ghsom

parameter_tests_density.ipynb

2

148859

{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "loading nmi score progress\n", "loading running time progress\n", "\n", ...

gpl-2.0

amygdala/tensorflow-workshop

workshop_sections/high_level_APIs/mnist_eager_keras-debug.ipynb

1

14437

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using Keras with TensorFlow eager mode, on the 'Fashion MNIST' dataset\n", "\n", "In this notebook, we'll use [TensorFlow's new eager execution mode](https://www.tensorflow.org/programmers_guide/eager)." ] }, { "cel...

apache-2.0

LucaCanali/Miscellaneous

Pyspark_SQL_Magic_Jupyter/IPython_Pyspark_SQL_Magic.ipynb

1

14926

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IPython magic functions for Pyspark\n", "# Examples of shortcuts for executing SQL in Spark" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "sourc...

apache-2.0

aitatanit/metatlas

4notebooks/old/examplenotebooks/MetAtlas_005_Find_Data_in_Grouped_Files_For_Given_Atlas.ipynb

1

3608986

null

bsd-3-clause

adrn/TwoFace

notebooks/figures/HighK-multimodal.ipynb

1

16764

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "from os import path\n", "\n", "# Third-party\n", "from astropy.io import fits\n", "from astropy.stats import median_absolute_deviation\n", "from astropy.t...

mit

pramitchoudhary/Experiments

notebook_gallery/other_experiments/build-models/model-selection-and-tuning/current-solutions/TPOT/TPOT-demo.ipynb

1

15275

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "_datascience": {}, "collapsed": false }, "outputs": [ { "data": { "text/html": [ "\n", " <iframe\n", " width=\"1000\"\n", " height=\"1000\"\n", ...

unlicense

camigord/Self-Driving-Car-Nanodegree

Camera_Calibration/camera_calibration.ipynb

1

1691676

null

mit

efoley/deep-learning

transfer-learning/Transfer_Learning.ipynb

3

23475

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Transfer Learning\n", "\n", "Most of the time you won't want to train a whole convolutional network yourself. Modern ConvNets training on huge datasets like ImageNet take weeks ...

mit

Am3ra/CS

CS4/Metodos/Generación de números aleatorios.ipynb

1

1174

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Alan Macedo Esparza\n", "A01366288" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[4 3 2 1 2 1 1 2...

mit

giacomov/astromodels

examples/EBL_attenuation_example.ipynb

2

35446

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# EBL Attenuation of a Spectral Model" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this notebook we create a simple *astromodels* spectrum and then apply EBL attenuation, as a function of redshif...

bsd-3-clause

parrt/msan501

notes/sound.ipynb

1

2087627

null

mit

jmhsi/justin_tinker

data_science/courses/cs231/simple_net_spiral_data.ipynb

1

6985

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "N = 100 # number of points per class\n", "D = 2 # dimensionality\n", "K = 3 # number of classes\n", "X = np.zeros((N*K,D)) # data matrix (each row = single example)\n", "y = np....

apache-2.0

Myllyenko/incubator-toree

etc/examples/notebooks/meetup-streaming-toree.ipynb

8

29770

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Streaming Meetups Dashboard" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The purpose of this notebook is to give an all-in-one demo of streaming data from the [meetup.com RSVP API](http://www.meet...

apache-2.0

IgorWang/MachineLearningPracticer

basic/Tree-Based Methods.ipynb

2

2650485

null

gpl-3.0

shngli/Data-Mining-Python

Google Scholar network/google scholar.ipynb

1

938395

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Google Scholar Visualization\n", "Credit: chengjun's scholarNetwork script " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualize Justin Wolfers' Google Scholar network" ] }, { ...

gpl-3.0

gklambauer/SelfNormalizingNetworks

SelfNormalizingNetworks_CNN_MNIST.ipynb

1

56613

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Tutorial on self-normalizing networks on the MNIST data set: convolutional neural networks\n", "\n", "*Author:* Guenter Klambauer, 2017\n", "\n", "tested under Python 3.5 and Tensorflow 1.1 ...

gpl-3.0

PhilHarnish/forge

src/puzzle/examples/mscpc/yr2016/journal_logs.ipynb

1

1708

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import forge\n", "from puzzle.puzzlepedia import puzzlepedia\n", "\n", "puzzle_dates = puzzlepedia.parse(\"\"\"\n", "01110 01111 00001 01001 10010\n", ...

mit

ZwickyTransientFacility/ztf_sim

notebooks/analyze_sim.ipynb

1

294354

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

bsd-3-clause

royalosyin/Python-Practical-Application-on-Climate-Variability-Studies

ex15-Trend and Anomaly Analyses of Long-term Tempro-Spatial Dataset.ipynb

1

295878

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<style>\n", "\n", ".rendered_html {\n", " font-family: \"proxima-nova\", helvetica;\n", " font-size: 130%;\n", " line-height...

mit

spacedrabbit/PythonBootcamp

Advanced Modules/Collections Module.ipynb

1

14356

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from collections import Counter" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { ...

mit

percyfal/bokeh

examples/howto/Range update callback.ipynb

1

3562

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from __future__ import division\n", "\n", "import numpy as np\n", "\n", "from bokeh.models import ColumnDataSource, CustomJS, Rect\n", "from bokeh...

bsd-3-clause

tensorflow/docs-l10n

site/en-snapshot/io/tutorials/orc.ipynb

2

9413

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "Tce3stUlHN0L" }, "source": [ "##### Copyright 2021 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "cellView": "form", "id":...

apache-2.0

ledeprogram/algorithms

class5/Simple_Linear_Regression.ipynb

1

73478

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt # package for doing plotting (necessary for adding the line)\n", "import stat...

gpl-3.0

ray-project/ray

doc/source/ray-contribute/docs.ipynb

1

20550

{ "cells": [ { "cell_type": "markdown", "id": "8c7dab40", "metadata": {}, "source": [ "(docs-contribute)=\n", "\n", "# Contributing to the Ray Documentation\n", "\n", "There are many ways to contribute to the Ray documentation, and we're always looking for new contributors.\n", "E...

apache-2.0

phoebe-project/phoebe2-docs

2.2/tutorials/requiv_crit_detached.ipynb

1

6075

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Critical Radii: Detached Systems\n", "============================\n", "\n", "Setup\n", "-----------------------------" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's first make s...

gpl-3.0

hide-tono/python-training

python-system-trade/sandbox/point_and_figure.ipynb

1

70519

{ "cells": [ { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "\n", "import matplotlib.finance as mpf\n", "import matplotlib.pyplot as plt\n", "import pandas\n", "from matplotlib.dates import d...

apache-2.0

jesserobertson/pynoddy

docs/notebooks/Sensitivity-Analysis.ipynb

1

255933

{ "metadata": { "name": "", "signature": "sha256:bfe0404ce8becc0369bb874843c773476bb9b85480c3ba4ef61b1c3a5bf5d5e3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sensitivity Analysis\n", "\n", ...

gpl-2.0

ucsc-astro/coffee

15_01_27_sqlalchemy/Examples.ipynb

1

116323

{ "metadata": { "name": "", "signature": "sha256:bdd723e0f126edb12e17b34d6123c51711e39d0daf416f1f0f7e431032ab2f2a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Learning SQLAlchemy\n", "\n", ...

gpl-3.0

ganguli-lab/single-trial

notebooks/Analysis - GP Manifold Recovery.ipynb

1

143490

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Relevant library" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "WARNING...

gpl-2.0

DavidPowell/OpenModes

test/Test MFIE.ipynb

1

4555

{ "metadata": { "name": "", "signature": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import os.path as osp\n", "\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", ...

gpl-3.0

tiagogiraldo/Machine_Learning_Nanodegree_Projects

student_intervention - 4.ipynb

1

46701

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Supervised Learning\n", "## Project 2: Building a Student Intervention System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Welcome to the secon...

gpl-3.0

dipanjank/ml

text_classification_and_clustering/step_3_classification_of_full_dataset.ipynb

1

244435

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align=\"center\">Level and Group Classification on Train and Test Datasets</h1>\n", "\n", "We have two classification tasks:\n", "\n", "* Predict the level, which ranges from 1-16.\n", "* Predict the group of a ...

gpl-3.0

hypergravity/cham_hates_python

exercise/cham_teaches_python_05_aplpy_healpy.ipynb

2

222287

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# APLpy basics\n", "\n", "[aplpyreaddocs](http://aplpy.readthedocs.io/en/stable/#)\n", "\n", "### - Introduction\n", "\n", " **APLpy : Astronomical Plotting Library in Python.**\n", "\n", " For conveni...

mit

fabianrost84/Rost-Rodrigo-Albors-et-al-2016

calculations/outgrowth.ipynb

1

265671

{ "cells": [ { "cell_type": "code", "execution_count": 191, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import scipy as sp\n", "from uncertainties import ufloat\n", "%matplotlib inline\n", ...

bsd-3-clause

mauriciogtec/PropedeuticoDataScience2017

Alumnos/Fernando_Briseno/.ipynb_checkpoints/Tarea_2_Fernando_Briseno-checkpoint.ipynb

1

3604695

null

mit

mtasende/Machine-Learning-Nanodegree-Capstone

notebooks/prod/n08_simple_q_learner_fast_learner_full_training.ipynb

1

271897

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# In this notebook a simple Q learner will be trained and evaluated. The Q learner recommends when to buy or sell shares of one particular stock, and in which quantity (in fact it determines the desired fracti...

mit

cpatrickalves/simprev

notebooks/CalculoEstoqueMetodoProb.ipynb

1

62576

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sugestão de metodologia para cálculo de Intervalos de Confiança \n", "\n", "Conforme mencionado na LDO de 2018, o modelo oficial do governo se define como determinístico: \n", "\n", "“[...] *ou seja, a partir da fixaç...

gpl-3.0

Z0m6ie/Zombie_Code

Data_Science_Course/Michigan Data Analysis Course/0 Introduction to Data Science in Python/Week4/Week+4.ipynb

1

52061

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "\n", "_You are currently looking at **version 1.0** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www...

mit

koverholt/notebooks

dask/create-cluster.ipynb

1

2533

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Create a Dask cluster using Coiled" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we'll create a Dask cluster with Coiled:" ] }, { "cell_type": "code", "execution_count": 1, ...

bsd-3-clause