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
jtemplon/2015-03-nit-analysis	notebooks/nit-bracketology-analysis.ipynb	1	43670	{ "metadata": { "name": "", "signature": "sha256:07bd9386b41b4e77d9cba4d0072de8ddeb5fc71d02e90d8e765a6cf50abd5b96" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing NIT Bracketology\n", "\...	mit
SamLau95/nbinteract	docs/notebooks/examples/examples_intro.ipynb	1	864	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples\n", "\n", "This section contains fleshed out example explanations that make use of\n", "`nbinteract`. Many of these examples were originally static pages taken from\n", "[UC Berkeley's Data 8 textbook][data8]...	bsd-3-clause
ryanpdwyer/brownian	brownian/ex/Brownian Workup.ipynb	1	129299	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import h5py\n", "import numpy as np\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import brownian\n", "from brownian import...	mit
astroumd/GradMap	notebooks/Lectures2019/Lecture1/GradMap_L1_Student.ipynb	1	60836	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to \"Doing Science\" in Python for REAL Beginners " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python is one of many languages you can use for research and HW purposes. In the next f...	gpl-3.0
etotheipluspi/ParticleFilters	src/Particle-Filter-Tutorial.ipynb	1	174432	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Loading help data...\n" ] } ], "source": [ "using Colladas\n", "using UrbanMaps\n...	mit
4dsolutions/Python5	GrapheneWithQrays.ipynb	1	17642	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Oregon Curriculum Network](http://www.4dsolutions.net/ocn) <br />\n", "[Discovering Math with Python](Introduction.ipynb)\n", "\n", "# Quadrays and Graphene\n", "\n", "<p><a href=\"https://commons.wikimedia.org/wik...	mit
aeroaks/PyFME	examples/examples_as_notebooks/example_003_stationary_ascent.ipynb	2	14287780	null	mit
ES-DOC/esdoc-jupyterhub	notebooks/niwa/cmip6/models/ukesm1-0-ll/aerosol.ipynb	1	84294	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "MIP Era: CMIP6 \n", "Institute: NIWA \n", "Source ID: UKESM1-0-LL \n", "**T...	gpl-3.0
mathnathan/notebooks	cmb_phase_plane/isc4220/Lab1.ipynb	1	35747	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ISC 4220\n", "## Lab 1 - Nonlinear Equations\n", "\n", "We import 3 handy libraries: python's standard numeric library numpy, a 'matlab like' plotting tool matplotlib, and a system tool for executive functions sys" ...	mit
sbussmann/sensor-fusion	Code/Rotate Sensor Data to Vehicle Reference Frame.ipynb	1	144476	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Goal: rotate XYZ signals to vehicle reference frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Experiment: I drove my car from home to Censio and back. My phone rested on my seat facing fo...	mit
ioshchepkov/SHTOOLS	examples/notebooks/tutorial_3.ipynb	1	1058914	null	bsd-3-clause
wrightaprilm/squamates	ExploratoryNotebooks/.ipynb_checkpoints/Change_counter-checkpoint.ipynb	1	4479	{ "metadata": { "name": "", "signature": "sha256:ba8c2f87fafbd8c0950c867f61ab0a4a9a7c2b5e64f334cb191c52a80b4cf6d4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import dendropy\n", "import pandas as...	mit
NlGG/Econometrics	TimeSeries/KalmanFilter.ipynb	1	128221	{ "cells": [ { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", ...	mit
abraia/abraia-python	notebooks/visual-similarity.ipynb	1	1715867	null	mit
PMEAL/OpenPNM	examples/reference/settings/understanding_reactive_transport_settings.ipynb	1	633	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Reactive Transport Settings" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" ...	mit
WillenZh/deep-learning-project	tutorials/embeddings/Skip-Grams-Solution.ipynb	71	813006	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Skip-gram word2vec\n", "\n", "In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in ...	mit
ES-DOC/esdoc-jupyterhub	notebooks/cas/cmip6/models/fgoals-g3/ocean.ipynb	1	164407	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "MIP Era: CMIP6 \n", "Institute: CAS \n", "Source ID: FGOALS-G3 \n", "*Topic...	gpl-3.0
WaltGurley/jupyter-notebooks-intro	Jupyter - interface.ipynb	1	7574	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using a notebook\n", "The purpose of this notebook is to introduce the Jupyter interface. This notebook is a guide to the Jupyter interface and writing code and text in Jupyter notebooks with the _Python_ programming language and...	mit
pdwyys20/deep-learning	UdacityNotes/3_CNN/cnn/ConvNets in TF.ipynb	1	6361	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Successfully downloaded train-images-idx3-ubyte.gz 9912422 bytes.\n", "Extracting ./train-images-idx3-ubyte.gz\n", "Successfu...	mit
Sevenan/liupengyuan.github.io	chapter2/homework/computer/4-19/201611680484.ipynb	15	3795	{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "输入字符串abcde\n", "edcba\n" ] } ], "source": [ "def reverse(s):\n", " print(s[le...	mit
empet/Math	Gamma-iterated-factorial.ipynb	1	417569	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##<center> Iterated complex factorial. Gamma function fractals </center>##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recently we came across a discussion on [*Gamma Function and Fractal Facto...	bsd-3-clause
Cyb3rWard0g/HELK	docker/helk-jupyter/notebooks/sigma/win_susp_dns_config.ipynb	1	3010	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DNS Server Error Failed Loading the ServerLevelPluginDLL\n", "This rule detects a DNS server error in which a specified plugin DLL (in registry) could not be loaded" ] }, { "cell_type": "markdown", "metadata": {}, ...	gpl-3.0
google/learned_optimization	docs/notebooks/Part3_Truncation_TruncatedStep.ipynb	1	216266	{ "cells": [ { "cell_type": "markdown", "id": "1bb8dc7b", "metadata": { "id": "1bb8dc7b" }, "source": [ "# Part 3: Truncation and TruncatedStep" ] }, { "cell_type": "code", "execution_count": 1, "id": "6ZVfhXllCTTe", "metadata": { "executionInfo": { "elapsed": 54, ...	apache-2.0
machinelearningnanodegree/stanford-cs231	assignments/assignment2/Dropout.ipynb	4	8912	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# Dropout\n", "Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to ...	mit
ThierryMondeel/FBA_python_tutorial	FBA_tutorials/5_biomarker_prediction_PKU.ipynb	1	6461	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Applying Shlomi et al's biomarker prediction method to PKU on RECON2\n", "\n", "<p>Authors: Thierry D.G.A Mondeel, Stefania Astrologo, Ewelina Weglarz-Tomczak & Hans V. Westerhoff <br/>\n", "University of Amsterdam <b...	mit
jrbourbeau/cr-composition	notebooks/legacy/radviz.ipynb	1	531951	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7/site-packages/setuptools-15.2-py2.7.egg',\n", ...	mit
hpssjellis/forth-tensorflow	r-examples/broken/a21-tkcanvas.ipynb	2	1487	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%autosave 0\n", "%cp RplotsOriginal.pdf Rplots.pdf \n", "%load r21.R" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "c...	mit
iABC2XYZ/abc	small/Untitled1.ipynb	1	642	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "a = tf.constant([1.0, 2.0])\n", "b = tf.constant([3.0, 4.0])\n", "c = a * b\n", "\n", "with tf.Session() as sess:\n", " print s...	gpl-3.0
ijstokes/bokeh-blaze-tutorial	solutions/1.4 Plotting - Climate (solution).ipynb	2	6021	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <img src=images/continuum_analytics_b&w.png align=\"left\" width=\"15%\" style=\"margin-right:15%\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align='center'>Bokeh Tutorial</h1>" ] }, ...	mit
cfjhallgren/shogun	doc/ipython-notebooks/computer_vision/Scene_classification.ipynb	5	29859	{ "metadata": { "name": "", "signature": "sha256:0c181f9f96b3b7fd14b5766e253aae3c37185ea81cfae909427b03d9993961fd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Visual Categorization with...	gpl-3.0
mwcraig/vpython-jupyter	Demos/Lorenz.ipynb	1	4020	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "require.undef(\"nbextensions/jquery-ui.custom.min\");" ], "text/plain": [ "<IPython.core.display.Javascript o...	mit
nafitzgerald/allennlp	tutorials/notebooks/embedding_tokens.ipynb	1	11930	{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "---\n", "layout: tutorial\n", "title: Embedding Tokens\n", "id: embedding-tokens\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook introduces how AllenNLP handles one of...	apache-2.0
cgivre/oreilly-sec-ds-fundamentals	Notebooks/Visualization/Data Visualization Worksheet.ipynb	2	11212	{ "cells": [ { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "plt.style.use('ggplot')" ] }, { ...	apache-2.0
guangtunbenzhu/BGT-Cosmology	Spectroscopy/.ipynb_checkpoints/eBOSStests-checkpoint.ipynb	1	164997	{ "metadata": { "name": "", "signature": "sha256:63d6f1f0dc32a7bb104161059acbb2f2263f2140c62f6a8bd61b464f217dfd14" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import datapath\n", "import fitsio\n"...	mit
aflaxman/siaman16-va-minitutorial	1-tutorial-notebooks/4-va_csmf.ipynb	1	10421	{ "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "pytho...	gpl-3.0
machinelearningnanodegree/stanford-cs231	solutions/kvn219/assignment1/knn.ipynb	1	709815	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# k-Nearest Neighbor (kNN) exercise\n", "\n", "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [a...	mit
datahac/jup	test/json-MN.ipynb	2	24464	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parsing JSON \n", "http://python-guide-pt-br.readthedocs.io/en/latest/scenarios/json/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example" ] }, { "cell_type": "code", "executi...	apache-2.0
rouxinol/HybridSystem	Simulation - CPW-Filter-qubit/.ipynb_checkpoints/Simulation Qubit-NR-CPW Roux-Hugo-checkpoint.ipynb	1	722561	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace f...	cc0-1.0
scikit-learn-contrib/hdbscan	notebooks/Looking at cluster consistency.ipynb	1	544245	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Looking at cluster consistency\n", "\n", "After clustering some data, you often want to (and should!) inspect the results. Obviously, visualization is a big part of this. It is also helpful to look at some representative ex...	bsd-3-clause
maestrotf/pymepps	examples/example_plot_stationnc.ipynb	1	29059	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Load station data based on NetCDF files\n", "\n", "In this example we show how to load station data based on NetCDF files.\n", "The data is loaded with the pymepps package. Thanks to Ingo Lange we could use original data ...	gpl-3.0
EdwardJKim/info490	week10/intro2sqldml.ipynb	2	65446	{ "metadata": { "name": "", "signature": "sha256:0fffe4793b0b481c1bf625aae04e032977c788abb008dd20461c365f5e1c4ce8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<DIV ALIGN=CENTER>\n", "\n", "...	mit
Aggieyixin/cjc2016	code/15.network_science.ipynb	1	301180	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "*\n", "\n", "# 网络科学简介\n", "\n", "*\n", "\n", "王成军 \n", "\n", "wangchengjun@nju.edu.cn\n", "\n", "计算传播网 http://computational-commu...	mit
QInfer/qinfer-examples	scoremixin_example.ipynb	1	69893	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fisher Score Mixin Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates how to use the ``qinfer.ScoreMixin`` class to develop models that use numerical differentiation t...	agpl-3.0
JKeun/project-02-watcha	02_preprocess/04_DESC(preprocess_df2(eng)).ipynb	1	683406	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# final_eng_df DESCRIPTION 및 변수간 분포 파악" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DESC" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true ...	mit
kejbaly2/metrique	notebooks/Cube Example - osinfo_rpm.ipynb	1	103889	{ "metadata": { "name": "", "signature": "sha256:788119d28ae731eb11a37404bc2971738c81f86c10ea8a61e780b10d0ce8f525" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from metrique import pyclient # main api i...	gpl-3.0
hoerldavid/nis-automation	transform_test.ipynb	1	9036	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\n", "\n", "\n", "from skimage.transform import AffineTransform\n", "import numpy as np\n", "from numpy.linalg import inv\n", "\n",...	mit
briangoodwin/nd101_udacityDeepLearning	tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb	2	29899	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# TV Script Generation\n", "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simps...	gpl-3.0
flohorovicic/pynoddy	docs/notebooks/Pynoddy_parallel_MC.ipynb	1	13024	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pynoddy parallelized MC error propagation\n", "\n", "This Notebook exemplifies the use of process parallelisation via the python `multiprocess` tool for parallelised MC error propagation and stochastic modeling.\n", "\n",...	gpl-2.0
vargaslo/msm-diffusion	Diffusion_MSM.ipynb	1	2200482	null	gpl-3.0
turbomanage/training-data-analyst	courses/machine_learning/deepdive/10_recommend/content_based_by_hand.ipynb	1	10403	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2NnuRIZedJmK" }, "source": [ "## Content Based Filtering by hand\n", "\n", "This lab illustrates how to implement a content based filter using low level Tensorflow operations. \n", "The code here f...	apache-2.0
EstevaoVieira/udacity_projects	titanic_survival_exploration/.ipynb_checkpoints/titanic_survival_exploration-checkpoint.ipynb	1	116798	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Introduction and Foundations\n", "## Project: Titanic Survival Exploration\n", "\n", "In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulti...	mit
mjbommar/scotus-predict	build_scdb_model_10_no_reset.ipynb	1	406691	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Predicting the Behavior of the Supreme Court of the United States: A General Approach\n", "==================\n", " * __Title__: Predicting the Behavior of the Supreme Court of the United States: A General Approach\n", " ...	apache-2.0
dodo325/school	Tim/plot.ipynb	1	42428	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "matplotlib.style.use('ggplot')" ] }, { "cell_type": "code", ...	apache-2.0
basnijholt/holoviews	examples/user_guide/Notebook_Magics.ipynb	1	9825	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook Magics" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import holoviews as hv\n", "from...	bsd-3-clause
ClaudioVZ/Teoria-FEM-Python	Teoría y ejemplos/trabajos virtuales viga.ipynb	1	18567	{ "metadata": { "name": "trabajos virtuales viga" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Principio de los trabajos virtuales para viga" ] }, { "cell_type": "markdow...	artistic-2.0
ottogroup/dstoolbox	notebooks/Examples_cluster.ipynb	1	4071	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using dstoolbox cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. [HierarchicalClusteri...	apache-2.0
salvor7/salvor7.github.io	content/A Million Pseudo-Random Digits/How uniformly Random are RAND's 'Million Psueod-Random Digits'.ipynb	1	17930	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "I found this [data set](https://www.kaggle.com/datacanary/a-million-pseudorandom-digits) on [Kaggle](http://www.kaggle.com). It is 1 million single digit numbers from 1 to 9, and I was curious if their distribution is uniform. Or said ...	mit
McIntyre-Lab/papers	fear_ase_2016/scripts/qsim_bayesian/qsim_Bayesian_Distributions_Multiimage.ipynb	1	8623	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "m...	lgpl-3.0
thehackerwithin/berkeley	code_examples/keras_introduction/Single_layer_by_hand.ipynb	1	6176	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building and training a single layer neural network by hand" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import packages\n", ...	bsd-3-clause
rjurney/Agile_Data_Code_2	ch09/Debugging Prediction Problems.ipynb	1	162952	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys, os, re\n", "import json\n", "import datetime, iso8601\n", "\n", "base_path = \"..\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##...	mit
Adobe-Marketing-Cloud/python-pandas-lab	.ipynb_checkpoints/Lesson #03ea -- Exercise Answers Manipulate Data-checkpoint.ipynb	1	1841	{ "metadata": { "name": "", "signature": "sha256:9ec1ff1d790d7cffcc6348d0fed674f97724768886dd6d5dc079f1172c836718" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Lesson #03 Exercise " ...	apache-2.0
mcocdawc/chemcoord	Tutorial/Advanced_customisation.ipynb	1	4519	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import chemcoord as cc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Settings" ] }, { "cell_type": "markdown", "metadat...	lgpl-3.0
deepmind/neural-processes	attentive_neural_process.ipynb	1	544117	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "opensource.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [], "last_runtime": { "build_target": "//learning/deepmind/dm_python:dm_notebook", "kind": "private" } ...	apache-2.0
rohinkumar/galsurveystudy	DR12G/DR12G_CMASS1_correl_V01_LCDM_rand.ipynb	1	446055	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation function of DR12G SDSS CMASS Catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import all the modules such as healpy and astropy needed for analyzing the structure" ] }, ...	mit
parambharat/ML-Programs	P2:_student_intervention/Grapher.ipynb	1	31575	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "from matplotlib import pylab\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { ...	mit
eds-uga/csci1360-fa16	lectures/L5.ipynb	1	24347	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Lecture 5: Loops\n", "\n", "CSCI 1360: Foundations for Informatics and Analytics" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "sli...	mit
mclaughlin6464/pearce	notebooks/Make Data for Redmagic HOD Fit.ipynb	1	30708	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from astropy.cosmology import LambdaCDM\n", "from astropy.io import fits" ] }, { "cell_type": "code", "execution_count": 2, ...	mit
mattwaite/JOUR491-Data-Visualization	Assignments/12_BubbleCharts/BubbleCharts.ipynb	1	239273	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bubble Charts: An in-class challenge\n", "\n", "A bubble chart is a scatterplot where the size of the bubble is scaled by some value. Bubble charts were made small f famous by the late [Hans Rosling in one of the most watched...	mit
ClaudioVZ/Teoria-FEM-Python	Matrices 3D/Multiplicacion.ipynb	1	94920	{ "metadata": { "name": "Matrices 3D" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Matrices 3D" ] }, { "cell_type": "heading", "level": 3, "...	artistic-2.0
mtmarsh2/vislab	image_style_experiments/ava style sklearn.ipynb	4	166331	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "df = dataset['val_df']\n", "yt = (df['label'] > 0).astype(int)\n", "yt_pred = np.zeros_like(yt).astype(float)\n", "...	bsd-2-clause
mldbai/mldb	container_files/tutorials/SELECT Tutorial.ipynb	1	114744	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SELECT Tutorial\n", "\n", "\n", "MLDB comes with a powerful implementation of [SQL's `SELECT` Queries](../../../../doc/#builtin/sql/Sql.md.html). This tutorial will walk you through the basics of `SELECT`, and some MLDB-s...	apache-2.0
mirjalil/DataScience	notebooks/clustering/hierarchical-clustering.ipynb	2	1997	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Hierarchical Clustering\n", "===" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example\n", "\n", "\|\|p1 \| p2 \| p3\|p4\|p5\|p6\|\n", "\|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\|:--:\|\n", "\|**p...	gpl-2.0
ReidAtcheson/laplace_screencast	.ipynb_checkpoints/ISPC enhanced Julia Compared to C-checkpoint.ipynb	1	98281	{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "using PyPlot\n", "include(\"julia/laplace1D_ispc.jl\")" ], "language": "python", "metadat...	mit
bicepjai/Deep-Survey-Text-Classification	deep_models/paper_01_cnn_sent_class/models.ipynb	1	121555	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:01.898998Z", "start_time": "2017-11-07T04:23:00.657322Z" } }, "outputs": []...	mit
CalculatedContent/tsvm	tsvm_gmm.ipynb	1	569085	{ "metadata": { "name": "", "signature": "sha256:55890931132935325af6dacb302669a7f6a6eba130d4fd63070df8a7a8460689" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas...	mit
Pritam-N/Linx	Test_Classification.ipynb	1	129290	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Test_Classification.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" ...	gpl-2.0
mayank-johri/LearnSeleniumUsingPython	Section 1 - Core Python/Chapter 01 - Introduction/01_Introduction.ipynb	1	28023	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 1 - Introduction to Python\n", "__________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Python](http://www.python.org) is a very **High Level, object-oriented, dynamic<sup>1</sup>, st...	gpl-3.0
jhillairet/ICRH	Reflectometry/E-plane bend with hyperbolic secant curvature.ipynb	2	85090	{ "metadata": { "name": "", "signature": "sha256:c63448ee57f1b69ae9a4a9f2f781c3eddb4c0c7b5af0b143f17d451666ae6d63" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "We remind here the expression of the curva...	mit
alepoydes/introduction-to-numerical-simulation	practice/matrix/schur.ipynb	1	10957	{"cells":[{"source":["# Разложение Шура\n","\n","Любую квадратную матрицу $A$ можно преобразованием $U$ подобия привести к верхнетреугольной матрице $R$,\n","причем преобразование $U$ можно выбрать унитарным (ортогональным для вещественных матриц):\n","$$A=URU^,\\quad U^AU=R,\\quad UU^=U^U=1,$$\n","такое представле...	mit
RyanAlberts/Springbaord-Capstone-Project	Statistics_Exercises/sliderule_dsi_inferential_statistics_exercise_1.ipynb	1	50544	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# What is the True Normal Human Body Temperature? \n", "\n", "#### Background\n", "\n", "The mean normal body temperature was held to be 37$^{\\circ}$C or 98.6$^{\\circ}$F for more than 120 years since it was first conc...	mit
eaton-lab/toytree	sandbox/ancient.ipynb	1	181611	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## IPCOAL WITH ANCIENT SAMPLES" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import ipcoal\n", "import toytree\n", "import msprime as ms" ] }, { ...	bsd-3-clause
eblur/dust	examples/mie_vs_RG_scattering.ipynb	1	41727	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "sourc...	bsd-2-clause
cbare/Etudes	notebooks/stock-trading-example.ipynb	1	1732760	null	apache-2.0
DTUWindEnergy/Python4WindEnergy	lesson 2/lecture-02.ipynb	1	29048	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Python Course, lesson 2\n", "#Advanced pure python" ] ...	apache-2.0
GoogleCloudPlatform/mlops-on-gcp	immersion/tfx_pipelines/04-metadata/labs/lab-04.ipynb	1	22108	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inspecting TFX metadata\n", "\n", "\n", "## Learning Objectives\n", "\n", "1. Use a GRPC server to access and analyze pipeline artifacts stored in the ML Metadata service of your AI Platform Pipelines instance.\n"...	apache-2.0
theandygross/HIV_Methylation	Setup/Read_HIV_Data.ipynb	1	95670	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Read HIV Data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "if os.getcwd().endswith('Setup'):\n", " os.ch...	mit
endlesspint8/endlesspint8.github.io	code/lynda-building-deep-learning-apps/06_keras_visualize.ipynb	1	5504	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", " - 0s - loss: 0.0588\n", "Epoch 2/50\n", " - 0s - loss: 0.0277\n", "Epoch 3/50\n", " - 0s -...	mit
flavio-casacurta/Nat2Py	Define Mapa.ipynb	1	1627	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from Util.DataPatterns import *" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source"...	mit
msyriac/orphics	scripts/EnlibResampleBug.ipynb	1	40154	{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The ...	bsd-2-clause
szigyi/DAT210x	Module4/.ipynb_checkpoints/solution_pca-checkpoint.ipynb	1	55831	{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", ...	mit
drublackberry/fantastic_demos	Titanic/Titanic.ipynb	1	587158	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook to process Kaggle's Titanic dataset\n", "\n", "This notebook uses the dataset from Kaggle's Titanic Comptetition to train a logistic regression and provides results with the test dataset\n", "https://www.kaggle.c...	mit
vi3k6i5/pandas_basics	2_b_apply_a_function_row_wise.ipynb	1	5732	{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "apply a function to dataframe rows" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "df = pd.DataFrame([['AA', 1]...	mit
tiffanyj41/hermes	notebooks/CF - Bayes, Pearson Correlation, etc.ipynb	3	212838	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing Collaborative Filtering Systems\n", "\n", "According to studies done by the article \"Comparing State-of-the-Art Collaborative Filtering Systems\" by Laurent Candillier, Frank Meyer, and Marc Boulle, the __best user...	apache-2.0
SHDShim/pytheos	examples/9_pv_eos_fit_scale_conversion.ipynb	1	847791	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source and citation\n", "\n", "- This notebook is a part of the `pytheos` package.\n", "- We...	apache-2.0
Dima806/udacity-mlnd-capstone	capstone-step3-final.ipynb	1	165139	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Udacity MLND Capstone Project\n", "## \"Determination of students’ interaction patterns with an intelligent tutoring system and study of their correlation with successful learning\"\n", "### Step 3 (comparison of learning rat...	apache-2.0
rishizsinha/project-beta	code/.ipynb_checkpoints/OpenFMRI modeling-checkpoint.ipynb	4	3262874	null	bsd-3-clause
gibiansky/blog	posts/fully-connected-neural-networks/post.ipynb	1	22070	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In the [previous post](http://andrew.gibiansky.com/blog/machine-learning/hessian-free-optimization/), we looked at Hessian-free optimization, a powerful optimization technique for training deep neural networks. In the next several, I'm...	gpl-2.0
longyangking/ML	Notes/preprocess/Iris Demo.ipynb	1	241734	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Iris Demo\n", "+ Check any null and invalid values\n", "+ Ensure the properties of features and labels\n", "+ Convert the string value into computational forms\n", "+ PCA -> Cluster Verification (optional)\n", "+ ...	lgpl-3.0
CenterForOpenScience/cas-overlay	OAuth2.ipynb	1	64895	{ "cells": [ { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import requests" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false },...	apache-2.0
nagamanicg/ml_lab_ecsc_306	labwork/lab1/TF_helloworld.ipynb	2	1417	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Simple hello worl...	apache-2.0

jtemplon/2015-03-nit-analysis

notebooks/nit-bracketology-analysis.ipynb

1

43670

{ "metadata": { "name": "", "signature": "sha256:07bd9386b41b4e77d9cba4d0072de8ddeb5fc71d02e90d8e765a6cf50abd5b96" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyzing NIT Bracketology\n", "\...

mit

SamLau95/nbinteract

docs/notebooks/examples/examples_intro.ipynb

1

864

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples\n", "\n", "This section contains fleshed out example explanations that make use of\n", "`nbinteract`. Many of these examples were originally static pages taken from\n", "[UC Berkeley's Data 8 textbook][data8]...

bsd-3-clause

ryanpdwyer/brownian

brownian/ex/Brownian Workup.ipynb

1

129299

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import h5py\n", "import numpy as np\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "import brownian\n", "from brownian import...

mit

astroumd/GradMap

notebooks/Lectures2019/Lecture1/GradMap_L1_Student.ipynb

1

60836

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to \"Doing Science\" in Python for REAL Beginners " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python is one of many languages you can use for research and HW purposes. In the next f...

gpl-3.0

etotheipluspi/ParticleFilters

src/Particle-Filter-Tutorial.ipynb

1

174432

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO: Loading help data...\n" ] } ], "source": [ "using Colladas\n", "using UrbanMaps\n...

mit

4dsolutions/Python5

GrapheneWithQrays.ipynb

1

17642

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Oregon Curriculum Network](http://www.4dsolutions.net/ocn) <br />\n", "[Discovering Math with Python](Introduction.ipynb)\n", "\n", "# Quadrays and Graphene\n", "\n", "<p><a href=\"https://commons.wikimedia.org/wik...

mit

aeroaks/PyFME

examples/examples_as_notebooks/example_003_stationary_ascent.ipynb

2

14287780

null

mit

ES-DOC/esdoc-jupyterhub

notebooks/niwa/cmip6/models/ukesm1-0-ll/aerosol.ipynb

1

84294

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "**MIP Era**: CMIP6 \n", "**Institute**: NIWA \n", "**Source ID**: UKESM1-0-LL \n", "**T...

gpl-3.0

mathnathan/notebooks

cmb_phase_plane/isc4220/Lab1.ipynb

1

35747

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ISC 4220\n", "## Lab 1 - Nonlinear Equations\n", "\n", "We import 3 handy libraries: python's standard numeric library *numpy*, a 'matlab like' plotting tool *matplotlib*, and a system tool for executive functions *sys*" ...

mit

sbussmann/sensor-fusion

Code/Rotate Sensor Data to Vehicle Reference Frame.ipynb

1

144476

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Goal: rotate XYZ signals to vehicle reference frame" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Experiment: I drove my car from home to Censio and back. My phone rested on my seat facing fo...

mit

ioshchepkov/SHTOOLS

examples/notebooks/tutorial_3.ipynb

1

1058914

null

bsd-3-clause

wrightaprilm/squamates

ExploratoryNotebooks/.ipynb_checkpoints/Change_counter-checkpoint.ipynb

1

4479

{ "metadata": { "name": "", "signature": "sha256:ba8c2f87fafbd8c0950c867f61ab0a4a9a7c2b5e64f334cb191c52a80b4cf6d4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import dendropy\n", "import pandas as...

mit

NlGG/Econometrics

TimeSeries/KalmanFilter.ipynb

1

128221

{ "cells": [ { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "code", ...

mit

abraia/abraia-python

notebooks/visual-similarity.ipynb

1

1715867

null

mit

PMEAL/OpenPNM

examples/reference/settings/understanding_reactive_transport_settings.ipynb

1

633

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Understanding Reactive Transport Settings" ] } ], "metadata": { "@webio": { "lastCommId": null, "lastKernelId": null }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" ...

mit

WillenZh/deep-learning-project

tutorials/embeddings/Skip-Grams-Solution.ipynb

71

813006

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Skip-gram word2vec\n", "\n", "In this notebook, I'll lead you through using TensorFlow to implement the word2vec algorithm using the skip-gram architecture. By implementing this, you'll learn about embedding words for use in ...

mit

ES-DOC/esdoc-jupyterhub

notebooks/cas/cmip6/models/fgoals-g3/ocean.ipynb

1

164407

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "**MIP Era**: CMIP6 \n", "**Institute**: CAS \n", "**Source ID**: FGOALS-G3 \n", "**Topic*...

gpl-3.0

WaltGurley/jupyter-notebooks-intro

Jupyter - interface.ipynb

1

7574

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using a notebook\n", "The purpose of this notebook is to introduce the Jupyter interface. This notebook is a guide to the Jupyter interface and writing code and text in Jupyter notebooks with the _Python_ programming language and...

mit

pdwyys20/deep-learning

UdacityNotes/3_CNN/cnn/ConvNets in TF.ipynb

1

6361

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

mit

Sevenan/liupengyuan.github.io

chapter2/homework/computer/4-19/201611680484.ipynb

15

3795

{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "输入字符串abcde\n", "edcba\n" ] } ], "source": [ "def reverse(s):\n", " print(s[le...

mit

empet/Math

Gamma-iterated-factorial.ipynb

1

417569

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "##<center> Iterated complex factorial. Gamma function fractals </center>##" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recently we came across a discussion on [*Gamma Function and Fractal Facto...

bsd-3-clause

Cyb3rWard0g/HELK

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

1

3010

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# DNS Server Error Failed Loading the ServerLevelPluginDLL\n", "This rule detects a DNS server error in which a specified plugin DLL (in registry) could not be loaded" ] }, { "cell_type": "markdown", "metadata": {}, ...

gpl-3.0

google/learned_optimization

docs/notebooks/Part3_Truncation_TruncatedStep.ipynb

1

216266

{ "cells": [ { "cell_type": "markdown", "id": "1bb8dc7b", "metadata": { "id": "1bb8dc7b" }, "source": [ "# Part 3: Truncation and TruncatedStep" ] }, { "cell_type": "code", "execution_count": 1, "id": "6ZVfhXllCTTe", "metadata": { "executionInfo": { "elapsed": 54, ...

apache-2.0

machinelearningnanodegree/stanford-cs231

assignments/assignment2/Dropout.ipynb

4

8912

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# Dropout\n", "Dropout [1] is a technique for regularizing neural networks by randomly setting some features to zero during the forward pass. In this exercise you will implement a dropout layer and modify your fully-connected network to ...

mit

ThierryMondeel/FBA_python_tutorial

FBA_tutorials/5_biomarker_prediction_PKU.ipynb

1

6461

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Applying Shlomi et al's biomarker prediction method to PKU on RECON2\n", "\n", "<p>**Authors**: Thierry D.G.A Mondeel, Stefania Astrologo, Ewelina Weglarz-Tomczak & Hans V. Westerhoff <br/>\n", "University of Amsterdam <b...

mit

jrbourbeau/cr-composition

notebooks/legacy/radviz.ipynb

1

531951

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['',\n", " '/cvmfs/icecube.opensciencegrid.org/py2-v1/RHEL_6_x86_64/lib/python2.7/site-packages/setuptools-15.2-py2.7.egg',\n", ...

mit

hpssjellis/forth-tensorflow

r-examples/broken/a21-tkcanvas.ipynb

2

1487

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%autosave 0\n", "%cp RplotsOriginal.pdf Rplots.pdf \n", "%load r21.R" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "c...

mit

iABC2XYZ/abc

small/Untitled1.ipynb

1

642

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf\n", "\n", "a = tf.constant([1.0, 2.0])\n", "b = tf.constant([3.0, 4.0])\n", "c = a * b\n", "\n", "with tf.Session() as sess:\n", " print s...

gpl-3.0

ijstokes/bokeh-blaze-tutorial

solutions/1.4 Plotting - Climate (solution).ipynb

2

6021

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <img src=images/continuum_analytics_b&w.png align=\"left\" width=\"15%\" style=\"margin-right:15%\">" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<h1 align='center'>Bokeh Tutorial</h1>" ] }, ...

mit

cfjhallgren/shogun

doc/ipython-notebooks/computer_vision/Scene_classification.ipynb

5

29859

{ "metadata": { "name": "", "signature": "sha256:0c181f9f96b3b7fd14b5766e253aae3c37185ea81cfae909427b03d9993961fd" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Visual Categorization with...

gpl-3.0

mwcraig/vpython-jupyter

Demos/Lorenz.ipynb

1

4020

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "application/javascript": [ "require.undef(\"nbextensions/jquery-ui.custom.min\");" ], "text/plain": [ "<IPython.core.display.Javascript o...

mit

nafitzgerald/allennlp

tutorials/notebooks/embedding_tokens.ipynb

1

11930

{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "---\n", "layout: tutorial\n", "title: Embedding Tokens\n", "id: embedding-tokens\n", "---" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook introduces how AllenNLP handles one of...

apache-2.0

cgivre/oreilly-sec-ds-fundamentals

Notebooks/Visualization/Data Visualization Worksheet.ipynb

2

11212

{ "cells": [ { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd \n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "%matplotlib inline\n", "plt.style.use('ggplot')" ] }, { ...

apache-2.0

guangtunbenzhu/BGT-Cosmology

Spectroscopy/.ipynb_checkpoints/eBOSStests-checkpoint.ipynb

1

164997

{ "metadata": { "name": "", "signature": "sha256:63d6f1f0dc32a7bb104161059acbb2f2263f2140c62f6a8bd61b464f217dfd14" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import datapath\n", "import fitsio\n"...

mit

aflaxman/siaman16-va-minitutorial

1-tutorial-notebooks/4-va_csmf.ipynb

1

10421

{ "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "pytho...

gpl-3.0

machinelearningnanodegree/stanford-cs231

solutions/kvn219/assignment1/knn.ipynb

1

709815

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# k-Nearest Neighbor (kNN) exercise\n", "\n", "*Complete and hand in this completed worksheet (including its outputs and any supporting code outside of the worksheet) with your assignment submission. For more details see the [a...

mit

datahac/jup

test/json-MN.ipynb

2

24464

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parsing JSON \n", "http://python-guide-pt-br.readthedocs.io/en/latest/scenarios/json/" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Example" ] }, { "cell_type": "code", "executi...

apache-2.0

rouxinol/HybridSystem

Simulation - CPW-Filter-qubit/.ipynb_checkpoints/Simulation Qubit-NR-CPW Roux-Hugo-checkpoint.ipynb

1

722561

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Populating the interactive namespace f...

cc0-1.0

scikit-learn-contrib/hdbscan

notebooks/Looking at cluster consistency.ipynb

1

544245

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Looking at cluster consistency\n", "\n", "After clustering some data, you often want to (and should!) inspect the results. Obviously, visualization is a big part of this. It is also helpful to look at some representative ex...

bsd-3-clause

maestrotf/pymepps

examples/example_plot_stationnc.ipynb

1

29059

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Load station data based on NetCDF files\n", "\n", "In this example we show how to load station data based on NetCDF files.\n", "The data is loaded with the pymepps package. Thanks to Ingo Lange we could use original data ...

gpl-3.0

EdwardJKim/info490

week10/intro2sqldml.ipynb

2

65446

{ "metadata": { "name": "", "signature": "sha256:0fffe4793b0b481c1bf625aae04e032977c788abb008dd20461c365f5e1c4ce8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<DIV ALIGN=CENTER>\n", "\n", "...

mit

Aggieyixin/cjc2016

code/15.network_science.ipynb

1

301180

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "***\n", "***\n", "# 网络科学简介\n", "***\n", "***\n", "\n", "王成军 \n", "\n", "wangchengjun@nju.edu.cn\n", "\n", "计算传播网 http://computational-commu...

mit

QInfer/qinfer-examples

scoremixin_example.ipynb

1

69893

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fisher Score Mixin Example" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates how to use the ``qinfer.ScoreMixin`` class to develop models that use numerical differentiation t...

agpl-3.0

JKeun/project-02-watcha

02_preprocess/04_DESC(preprocess_df2(eng)).ipynb

1

683406

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# final_eng_df DESCRIPTION 및 변수간 분포 파악" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DESC" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true ...

mit

kejbaly2/metrique

notebooks/Cube Example - osinfo_rpm.ipynb

1

103889

{ "metadata": { "name": "", "signature": "sha256:788119d28ae731eb11a37404bc2971738c81f86c10ea8a61e780b10d0ce8f525" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from metrique import pyclient # main api i...

gpl-3.0

hoerldavid/nis-automation

transform_test.ipynb

1

9036

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import logging\n", "\n", "\n", "from skimage.transform import AffineTransform\n", "import numpy as np\n", "from numpy.linalg import inv\n", "\n",...

mit

briangoodwin/nd101_udacityDeepLearning

tv-script-generation/.ipynb_checkpoints/dlnd_tv_script_generation-checkpoint.ipynb

2

29899

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# TV Script Generation\n", "In this project, you'll generate your own [Simpsons](https://en.wikipedia.org/wiki/The_Simpsons) TV scripts using RNNs. You'll be using part of the [Simps...

gpl-3.0

flohorovicic/pynoddy

docs/notebooks/Pynoddy_parallel_MC.ipynb

1

13024

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# pynoddy parallelized MC error propagation\n", "\n", "This Notebook exemplifies the use of process parallelisation via the python `multiprocess` tool for parallelised MC error propagation and stochastic modeling.\n", "\n",...

gpl-2.0

vargaslo/msm-diffusion

Diffusion_MSM.ipynb

1

2200482

null

gpl-3.0

turbomanage/training-data-analyst

courses/machine_learning/deepdive/10_recommend/content_based_by_hand.ipynb

1

10403

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "2NnuRIZedJmK" }, "source": [ "## Content Based Filtering by hand\n", "\n", "This lab illustrates how to implement a content based filter using low level Tensorflow operations. \n", "The code here f...

apache-2.0

EstevaoVieira/udacity_projects

titanic_survival_exploration/.ipynb_checkpoints/titanic_survival_exploration-checkpoint.ipynb

1

116798

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Engineer Nanodegree\n", "## Introduction and Foundations\n", "## Project: Titanic Survival Exploration\n", "\n", "In 1912, the ship RMS Titanic struck an iceberg on its maiden voyage and sank, resulti...

mit

mjbommar/scotus-predict

build_scdb_model_10_no_reset.ipynb

1

406691

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Predicting the Behavior of the Supreme Court of the United States: A General Approach\n", "==================\n", " * __Title__: Predicting the Behavior of the Supreme Court of the United States: A General Approach\n", " ...

apache-2.0

dodo325/school

Tim/plot.ipynb

1

42428

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import matplotlib\n", "matplotlib.style.use('ggplot')" ] }, { "cell_type": "code", ...

apache-2.0

basnijholt/holoviews

examples/user_guide/Notebook_Magics.ipynb

1

9825

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook Magics" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np\n", "import holoviews as hv\n", "from...

bsd-3-clause

ClaudioVZ/Teoria-FEM-Python

Teoría y ejemplos/trabajos virtuales viga.ipynb

1

18567

{ "metadata": { "name": "trabajos virtuales viga" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Principio de los trabajos virtuales para viga" ] }, { "cell_type": "markdow...

artistic-2.0

ottogroup/dstoolbox

notebooks/Examples_cluster.ipynb

1

4071

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Using dstoolbox cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Table of contents" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "1. [HierarchicalClusteri...

apache-2.0

salvor7/salvor7.github.io

content/A Million Pseudo-Random Digits/How uniformly Random are RAND's 'Million Psueod-Random Digits'.ipynb

1

17930

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "I found this [data set](https://www.kaggle.com/datacanary/a-million-pseudorandom-digits) on [Kaggle](http://www.kaggle.com). It is 1 million single digit numbers from 1 to 9, and I was curious if their distribution is uniform. Or said ...

mit

McIntyre-Lab/papers

fear_ase_2016/scripts/qsim_bayesian/qsim_Bayesian_Distributions_Multiimage.ipynb

1

8623

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

lgpl-3.0

thehackerwithin/berkeley

code_examples/keras_introduction/Single_layer_by_hand.ipynb

1

6176

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Building and training a single layer neural network by hand" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Import packages\n", ...

bsd-3-clause

rjurney/Agile_Data_Code_2

ch09/Debugging Prediction Problems.ipynb

1

162952

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import sys, os, re\n", "import json\n", "import datetime, iso8601\n", "\n", "base_path = \"..\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##...

mit

Adobe-Marketing-Cloud/python-pandas-lab

.ipynb_checkpoints/Lesson #03ea -- Exercise Answers Manipulate Data-checkpoint.ipynb

1

1841

{ "metadata": { "name": "", "signature": "sha256:9ec1ff1d790d7cffcc6348d0fed674f97724768886dd6d5dc079f1172c836718" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Lesson #03 Exercise " ...

apache-2.0

mcocdawc/chemcoord

Tutorial/Advanced_customisation.ipynb

1

4519

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import chemcoord as cc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Settings" ] }, { "cell_type": "markdown", "metadat...

lgpl-3.0

deepmind/neural-processes

attentive_neural_process.ipynb

1

544117

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "opensource.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [], "last_runtime": { "build_target": "//learning/deepmind/dm_python:dm_notebook", "kind": "private" } ...

apache-2.0

rohinkumar/galsurveystudy

DR12G/DR12G_CMASS1_correl_V01_LCDM_rand.ipynb

1

446055

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Correlation function of DR12G SDSS CMASS Catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First import all the modules such as healpy and astropy needed for analyzing the structure" ] }, ...

mit

parambharat/ML-Programs

P2:_student_intervention/Grapher.ipynb

1

31575

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

mit

eds-uga/csci1360-fa16

lectures/L5.ipynb

1

24347

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Lecture 5: Loops\n", "\n", "CSCI 1360: Foundations for Informatics and Analytics" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "sli...

mit

mclaughlin6464/pearce

notebooks/Make Data for Redmagic HOD Fit.ipynb

1

30708

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import numpy as np\n", "from astropy.cosmology import LambdaCDM\n", "from astropy.io import fits" ] }, { "cell_type": "code", "execution_count": 2, ...

mit

mattwaite/JOUR491-Data-Visualization

Assignments/12_BubbleCharts/BubbleCharts.ipynb

1

239273

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bubble Charts: An in-class challenge\n", "\n", "A bubble chart is a scatterplot where the size of the bubble is scaled by some value. Bubble charts were made small f famous by the late [Hans Rosling in one of the most watched...

mit

ClaudioVZ/Teoria-FEM-Python

Matrices 3D/Multiplicacion.ipynb

1

94920

{ "metadata": { "name": "Matrices 3D" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Matrices 3D" ] }, { "cell_type": "heading", "level": 3, "...

artistic-2.0

mtmarsh2/vislab

image_style_experiments/ava style sklearn.ipynb

4

166331

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "df = dataset['val_df']\n", "yt = (df['label'] > 0).astype(int)\n", "yt_pred = np.zeros_like(yt).astype(float)\n", "...

bsd-2-clause

mldbai/mldb

container_files/tutorials/SELECT Tutorial.ipynb

1

114744

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# SELECT Tutorial\n", "\n", "\n", "MLDB comes with a powerful implementation of [SQL's `SELECT` Queries](../../../../doc/#builtin/sql/Sql.md.html). This tutorial will walk you through the basics of `SELECT`, and some MLDB-s...

apache-2.0

mirjalil/DataScience

notebooks/clustering/hierarchical-clustering.ipynb

2

1997

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Hierarchical Clustering\n", "===" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Example\n", "\n", "||p1 | p2 | p3|p4|p5|p6|\n", "|:--:|:--:|:--:|:--:|:--:|:--:|:--:|\n", "|**p...

gpl-2.0

ReidAtcheson/laplace_screencast

.ipynb_checkpoints/ISPC enhanced Julia Compared to C-checkpoint.ipynb

1

98281

{ "metadata": { "language": "Julia", "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "using PyPlot\n", "include(\"julia/laplace1D_ispc.jl\")" ], "language": "python", "metadat...

mit

bicepjai/Deep-Survey-Text-Classification

deep_models/paper_01_cnn_sent_class/models.ipynb

1

121555

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "ExecuteTime": { "end_time": "2017-11-07T04:23:01.898998Z", "start_time": "2017-11-07T04:23:00.657322Z" } }, "outputs": []...

mit

CalculatedContent/tsvm

tsvm_gmm.ipynb

1

569085

{ "metadata": { "name": "", "signature": "sha256:55890931132935325af6dacb302669a7f6a6eba130d4fd63070df8a7a8460689" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import pandas...

mit

Pritam-N/Linx

Test_Classification.ipynb

1

129290

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "Test_Classification.ipynb", "version": "0.3.2", "provenance": [], "collapsed_sections": [], "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" ...

gpl-2.0

mayank-johri/LearnSeleniumUsingPython

Section 1 - Core Python/Chapter 01 - Introduction/01_Introduction.ipynb

1

28023

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Chapter 1 - Introduction to Python\n", "__________" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "[Python](http://www.python.org) is a very **High Level, object-oriented, dynamic<sup>1</sup>, st...

gpl-3.0

jhillairet/ICRH

Reflectometry/E-plane bend with hyperbolic secant curvature.ipynb

2

85090

{ "metadata": { "name": "", "signature": "sha256:c63448ee57f1b69ae9a4a9f2f781c3eddb4c0c7b5af0b143f17d451666ae6d63" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "We remind here the expression of the curva...

mit

alepoydes/introduction-to-numerical-simulation

practice/matrix/schur.ipynb

1

10957

{"cells":[{"source":["# Разложение Шура\n","\n","Любую квадратную матрицу $A$ можно преобразованием $U$ подобия привести к верхнетреугольной матрице $R$,\n","причем преобразование $U$ можно выбрать унитарным (ортогональным для вещественных матриц):\n","$$A=URU^*,\\quad U^*AU=R,\\quad UU^*=U^*U=1,$$\n","такое представле...

mit

RyanAlberts/Springbaord-Capstone-Project

Statistics_Exercises/sliderule_dsi_inferential_statistics_exercise_1.ipynb

1

50544

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# What is the True Normal Human Body Temperature? \n", "\n", "#### Background\n", "\n", "The mean normal body temperature was held to be 37$^{\\circ}$C or 98.6$^{\\circ}$F for more than 120 years since it was first conc...

mit

eaton-lab/toytree

sandbox/ancient.ipynb

1

181611

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## IPCOAL WITH ANCIENT SAMPLES" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import ipcoal\n", "import toytree\n", "import msprime as ms" ] }, { ...

bsd-3-clause

eblur/dust

examples/mie_vs_RG_scattering.ipynb

1

41727

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

bsd-2-clause

cbare/Etudes

notebooks/stock-trading-example.ipynb

1

1732760

null

apache-2.0

DTUWindEnergy/Python4WindEnergy

lesson 2/lecture-02.ipynb

1

29048

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Python Course, lesson 2\n", "#Advanced pure python" ] ...

apache-2.0

GoogleCloudPlatform/mlops-on-gcp

immersion/tfx_pipelines/04-metadata/labs/lab-04.ipynb

1

22108

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Inspecting TFX metadata\n", "\n", "\n", "## Learning Objectives\n", "\n", "1. Use a GRPC server to access and analyze pipeline artifacts stored in the ML Metadata service of your AI Platform Pipelines instance.\n"...

apache-2.0

theandygross/HIV_Methylation

Setup/Read_HIV_Data.ipynb

1

95670

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Read HIV Data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "if os.getcwd().endswith('Setup'):\n", " os.ch...

mit

endlesspint8/endlesspint8.github.io

code/lynda-building-deep-learning-apps/06_keras_visualize.ipynb

1

5504

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch 1/50\n", " - 0s - loss: 0.0588\n", "Epoch 2/50\n", " - 0s - loss: 0.0277\n", "Epoch 3/50\n", " - 0s -...

mit

flavio-casacurta/Nat2Py

Define Mapa.ipynb

1

1627

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from Util.DataPatterns import *" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": true }, "outputs": [], "source"...

mit

msyriac/orphics

scripts/EnlibResampleBug.ipynb

1

40154

{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "autoscroll": false, "ein.hycell": false, "ein.tags": "worksheet-0", "slideshow": { "slide_type": "-" } }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The ...

bsd-2-clause

szigyi/DAT210x

Module4/.ipynb_checkpoints/solution_pca-checkpoint.ipynb

1

55831

{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sklearn.decomposition import PCA\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", ...

mit

drublackberry/fantastic_demos

Titanic/Titanic.ipynb

1

587158

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notebook to process Kaggle's Titanic dataset\n", "\n", "This notebook uses the dataset from Kaggle's Titanic Comptetition to train a logistic regression and provides results with the test dataset\n", "https://www.kaggle.c...

mit

vi3k6i5/pandas_basics

2_b_apply_a_function_row_wise.ipynb

1

5732

{ "cells": [ { "cell_type": "raw", "metadata": {}, "source": [ "apply a function to dataframe rows" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import pandas as pd\n", "df = pd.DataFrame([['AA', 1]...

mit

tiffanyj41/hermes

notebooks/CF - Bayes, Pearson Correlation, etc.ipynb

3

212838

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comparing Collaborative Filtering Systems\n", "\n", "According to studies done by the article \"Comparing State-of-the-Art Collaborative Filtering Systems\" by Laurent Candillier, Frank Meyer, and Marc Boulle, the __best user...

apache-2.0

SHDShim/pytheos

examples/9_pv_eos_fit_scale_conversion.ipynb

1

847791

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Source and citation\n", "\n", "- This notebook is a part of the `pytheos` package.\n", "- We...

apache-2.0

Dima806/udacity-mlnd-capstone

capstone-step3-final.ipynb

1

165139

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Udacity MLND Capstone Project\n", "## \"Determination of students’ interaction patterns with an intelligent tutoring system and study of their correlation with successful learning\"\n", "### Step 3 (comparison of learning rat...

apache-2.0

rishizsinha/project-beta

code/.ipynb_checkpoints/OpenFMRI modeling-checkpoint.ipynb

4

3262874

null

bsd-3-clause

gibiansky/blog

posts/fully-connected-neural-networks/post.ipynb

1

22070

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In the [previous post](http://andrew.gibiansky.com/blog/machine-learning/hessian-free-optimization/), we looked at Hessian-free optimization, a powerful optimization technique for training deep neural networks. In the next several, I'm...

gpl-2.0

longyangking/ML

Notes/preprocess/Iris Demo.ipynb

1

241734

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Iris Demo\n", "+ Check any null and invalid values\n", "+ Ensure the properties of features and labels\n", "+ Convert the string value into computational forms\n", "+ PCA -> Cluster Verification (optional)\n", "+ ...

lgpl-3.0

CenterForOpenScience/cas-overlay

OAuth2.ipynb

1

64895

{ "cells": [ { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import json\n", "import requests" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false, "scrolled": false },...

apache-2.0

nagamanicg/ml_lab_ecsc_306

labwork/lab1/TF_helloworld.ipynb

2

1417

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import tensorflow as tf" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Simple hello worl...

apache-2.0