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
knowledgeanyhow/notebooks	airline/Exploration of Airline On-Time Performance.ipynb	2	375407	{ "metadata": { "name": "", "signature": "sha256:4997000efd69b335ff55f6eed3b9ee20a47bb92f0fd25ec1119af0e633fbe289" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploration of Airline On-Time Performan...	mit
Ledoux/ShareYourSystem	Ouvaton/Concluder.ipynb	1	7082	{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Concluder\n\n##Doc\n----\n\n\n> \n> A Concluder\n> \n> \n\n----\n\n<small>\nView the Concluder notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Concluder.ipyn...	mit
NYUDataBootcamp/Projects	UG_F16/RodriguezBallve-Spain's_Labor_Market.ipynb	1	292917	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploring Spain's Broken Labor Market" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Author Bosco Rodríguez Ballvé\n", "Date Fall 2016\n", "Class Data Bootcamp @ NYU Stern \n", ...	mit
tensorflow/probability	tensorflow_probability/python/experimental/nn/examples/vib_dose.ipynb	1	185056	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XXDeo-aGOAXF" }, "source": [ "##### Copyright 2020 The TensorFlow Authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "executi...	apache-2.0
mne-tools/mne-tools.github.io	dev/_downloads/c6baf7c1a2f53fda44e93271b91f45b8/50_beamformer_lcmv.ipynb	1	15968	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Source rec...	bsd-3-clause
AaltoML/kalman-jax	kalmanjax/notebooks/2d_log_gaussian_cox_process.ipynb	1	157430	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2D Log-Gaussian Cox Process via Spatio-Temporal Kalman Smoothing\n", "\n", "## Import and load data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout...	apache-2.0
4dsolutions/Python5	Comparing JavaScript with Python.ipynb	1	11516	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Python for Everyone!<br/>[Oregon Curriculum Network](http://4dsolutions.net/ocn/)\n", "\n", "## Python and JavaScript\n", "\n", "JavaScript has been moving [a lot closer to Python](http://worldgame.blogspot.com/2016/10/...	mit
WOnder93/pbkdf2-gpu	data/metacentrum/graphs.ipynb	1	680583	{ "metadata": { "name": "", "signature": "sha256:c97123e1d02797e16b0ef122721699efffa69f1c66333f77596b14d6dc9db5a8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "f...	gpl-2.0
alepoydes/introduction-to-numerical-simulation	practice/What does mean mean mean.ipynb	1	365364	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Вычисление сумм" ] ...	mit
yuvrajsingh86/DeepLearning_Udacity	sentiment-network/Sentiment_Classification_Projects.ipynb	1	99598	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", "\n", "by Andrew Trask\n", "\n", "- Twitter: @iamtrask\n", "- Blog: http://iamtrask.github.io" ] }, { "cell_type": "m...	mit
mne-tools/mne-tools.github.io	0.15/_downloads/plot_brainstorm_data.ipynb	1	3144	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# Brainstorm tutoria...	bsd-3-clause
hmenke/pairinteraction	doc/sphinx/examples_python/vdw_near_surface.ipynb	3	25578	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dispersion Coefficients Near Surfaces" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial we reproduce the results depicted in Figure 5 from J. Block and S. Scheel \"van der Waals intera...	gpl-3.0
xbsd/CS109	content_raj/HW1-Copy0.ipynb	1	37311	{ "metadata": { "name": "", "signature": "sha256:3eabce1a0b9c3e1a9ee432ec43b13290cab7e68011597e38d675bc10224aa3ef" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework 1. Which of two things is large...	mit
metpy/MetPy	v0.9/_downloads/ef4bfbf049be071a6c648d7918a50105/Simple_Sounding.ipynb	1	4765	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nSimple Soundin...	bsd-3-clause
intel-analytics/BigDL	apps/ray/parameter_server/sharded_parameter_server.ipynb	1	15424	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# This notebook is adapted from: \n", "https://github.com/ray-project/tutorial/tree/master/examples/sharded_parameter_server.ipynb\n", "\n", "# Sharded Parameter Servers\n", "\n", "GOAL: The goal of this exercis...	apache-2.0
Zhang-O/small	tensor__cpu/skimages/get_started.ipynb	2	336126	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import skimage\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "# %matplotlib\n", "%matplotlib inline " ] }, { "cell_type": "code", "execution_count"...	mit
jeroenjanssens/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers	Chapter5_LossFunctions/LossFunctions.ipynb	4	1358823	null	mit
roatienza/Deep-Learning-Experiments	versions/2020/keras/cnn/cnn-siamese.ipynb	1	14604	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Implements a Siamese/Y-Network using Functional API\n", "\n", "~99.4% test accuracy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output...	mit
root-mirror/training	SummerStudentCourse/2019/Exercises/ROOTBooks/CentralLimitTheorem_Solution.ipynb	5	50466	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise: Central Limit Theorem\n", "\n", "In this exercise we will show what is the Central Limit Theorem and how it applies " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 1: Gene...	gpl-2.0
llooker/public-datasets-pipelines	samples/tutorial.ipynb	3	6210	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "7i8lsRFe2lu5" }, "source": [ "# Overview\n", "\n", "Add a brief description of this tutorial here." ] }, { "cell_type": "code", "execution_count": null, "metadata": ...	apache-2.0
stereoboy/Study	Issues/algorithms/Linked Lists.ipynb	1	6428	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Add two numbers represented by linked lists" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Node():\n", " def __init__(s...	mit
ocefpaf/folium	examples/plugin-patterns.ipynb	2	137190	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preleminary demo of the pattern plugin for Folium" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style=\"width:100%;\"><div st...	mit
TimothyADavis/KinMSpy	kinms/docs/KinMSpy_tutorial.ipynb	1	227468	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# KinMS galaxy fitting tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial aims at getting you up and running with galaxy kinematic modelling using _KinMS_! To start you will need to...	mit
napsternxg/ipython-notebooks	Monte Carlo Integration.ipynb	1	232991	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Monte Carlo Integration\n", "\n", "Inspired from the following posts:\n", "\n", "* http://nbviewer.jupyter.org/github/cs109/content/blob/master/labs/lab7/GibbsSampler.ipynb\n", "* http://twiecki.gi...	apache-2.0
mackst/myPyTutorialNotebook	21 类实战(1).ipynb	1	30736	{ "metadata": { "name": "21 \u7c7b\u5b9e\u6218(1)" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "point = Point(2, 3, 4)\n", "point.x # 2\n", "point.y # 3\n", "point.z # 4\n", "\n", ...	mit
Neuroglycerin/neukrill-net-work	notebooks/Checking resumed 40aug and 16aug models.ipynb	1	981797	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using gpu device 2: Tesla K40c\n", ":0: FutureWarning: IPython widgets are experimental and may change i...	mit
linncy/Tester-Automation	cm22c.ipynb	1	2489	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Device Found.\n", "Error\n" ] } ], "source": [ "import visa\n", "rm = visa.ResourceManager()\n", "if ...	mit
jimthompson5802/kaggle-BNP-Paribas	src/sandbox/test_model_parms_retrieval.ipynb	1	2616	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/jim/Desktop/Kaggle/BNPParibasCardif/src/sandbox\n" ] } ], "source": [ "import pandas...	mit
jdvelasq/machine-learning	old/ML-00-scraping.ipynb	1	46289	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Aprendizaje de Máquinas -- 0 -- Scraping\n", "Notas de clase sobre aprendizaje de máquinas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Juan David Velásquez Henao \n", "jdvelasq@unal...	mit
ES-DOC/esdoc-jupyterhub	notebooks/nasa-giss/cmip6/models/giss-e2-1h/aerosol.ipynb	1	84302	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "MIP Era: CMIP6 \n", "Institute: NASA-GISS \n", "Source ID: GISS-E2-1H \n", ...	gpl-3.0
ES-DOC/esdoc-jupyterhub	notebooks/cnrm-cerfacs/cmip6/models/cnrm-cm6-1/seaice.ipynb	1	99821	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "MIP Era: CMIP6 \n", "Institute: CNRM-CERFACS \n", "Source ID: CNRM-CM6-1 \n", ...	gpl-3.0
ledeprogram/algorithms	class9/homework/Kandrach_Sasha_9_3.ipynb	1	4467	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import preprocessing\n", "from sklearn import cross_validation\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.feature_...	gpl-3.0
google/starthinker	colabs/cm360_segmentology.ipynb	1	15783	{ "license": "Licensed under the Apache License, Version 2.0", "copyright": "Copyright 2020 Google LLC", "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "CM360 Segmentology", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernelspec...	apache-2.0
aborgher/Main-useful-functions-for-ML	.ipynb_checkpoints/NLP-checkpoint.ipynb	1	67248	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Correction-with-enchant\" data-toc-modified-id=\"Correction-with-enchant-1\"><span class=\"toc-item-num\">1  </span>Correction wit...	gpl-3.0
ES-DOC/esdoc-jupyterhub	notebooks/nerc/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: NERC \n", "Source ID: UKESM1-0-LL \n", "**T...	gpl-3.0
qinwf-nuan/keras-js	notebooks/pipeline/pipeline_10.ipynb	1	27682	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import json\n"...	mit
google-research/google-research	prime/prime_colab.ipynb	1	109093	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "PA8SSKskdEyX" }, "source": [ "\n", "Copyright 2022 Google LLC.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", ...	apache-2.0
UCBerkeleySETI/blml	gbt_cluster/simple_feature_clustering.ipynb	1	9712776	null	mit
FourthCohortAwesome/NightThree	Night3_DTK.ipynb	1	4446	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Similar type issue from last weeks problem\n", "3 File protocols now\n", "\n", "Protocol1 = 7Col x 3Row --> Alt1\n", "Protocol2 = 2Col x 9Row --> Alt2\n", "Protocol3 = 4Col x 7Row --> Alt3\n", "\n", "File ro...	mit
asimihsan/pydata-ldn2014-writeup	00 - Summary, Highlights, Readings and Order.ipynb	1	20180	{ "metadata": { "name": "", "signature": "sha256:2c00f0ddb7215d7c4cea19200a00bf8d1c743b2cbb0d2fadb582c83fab517cbf" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Intro\n", "\n", "Below is:\...	mit
minh5/cpsc	reports/neiss.ipynb	1	37331	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev2 toc-item\"><a href=\"#Are-there-products-we-should-be-aware-of?\" data-toc-modified-id=\"Are-there-products-we-should-be-aware-of?-01\"><span class=\"toc-item-num...	mit
JAmarel/Phys202	Integration/IntegrationEx02.ipynb	2	11908	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Integration Exercise 2" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "met...	mit
ES-DOC/esdoc-jupyterhub	notebooks/inm/cmip6/models/sandbox-2/ocean.ipynb	1	164407	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "MIP Era: CMIP6 \n", "Institute: INM \n", "Source ID: SANDBOX-2 \n", "*Topic...	gpl-3.0
mmckerns/tutmom	solutions.ipynb	2	16237	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solutions to exercises" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "EXERCISE: Solve the constrained programming problem by any of the means above.\n", "\n", "...	bsd-3-clause
williamdjones/protein_binding	notebooks/Neural Network (In Progress).ipynb	1	17477	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import time\n", "import glob\n", "...	mit
Krastanov/cutiepy	examples/Lindblad_Master_Equation_Solver_Examples.ipynb	1	108040	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Table of Contents\n", "\n", "1. [Rabi Oscillations](#Rabi-Oscillations)\n", " 1. [Simulating the Full Hamiltonian](#Simulating-the-Full-Hamiltonian)\n", " 2. [With Rotating Wave Approximation](#With-Rotating-W...	bsd-3-clause
Gorgel/minkpy	analysis/notebooks/V3_analyser_xibox.ipynb	1	270297	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "import pylab\n", "from scipy...	gpl-2.0
zzsza/Datascience_School	도커 Tip - 컨테이너의 파일 백업.ipynb	1	6913	{ "cells": [ { "cell_type": "markdown", "metadata": { "school_cell_uuid": "da230397189449d98d3f2f9b8d3da5ae" }, "source": [ "# 도커 Tip - 컨테이너의 파일 백업" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "ba6f3bd3fa4e40b3846186c8a6019abf" }, "source": [ "도커 이...	mit
TANAV/predictorsAndDB	predictorNotebooks/SGDClassifier_Jewelry.ipynb	1	12531	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import cPickle\n", "from scipy.io import loadmat\n", "from sklearn.linear_model import SGDClassifier\n", "from skle...	apache-2.0
albahnsen/ML_SecurityInformatics	notebooks/14-KaggleCompetition.ipynb	1	31809	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 14 - Kaggle Competition\n", "# Fraud Detection\n", "\n", "\n", "## https://inclass.kaggle.com/c/easy-ml-class\n", "\n", "by [Alejandro Correa Bahnsen](albahnsen.com/)\n", "\n", "version 0.1, May 2016\n...	mit
GoogleCloudPlatform/vertex-ai-samples	notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb	1	33506	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "#...	apache-2.0
lasersonlab/pepsyn	workflows/metagenomic_shotgun_single_end.ipynb	1	231979	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Shotgun metagenomic single end library" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from os.path import join as pjoin\n", "imp...	apache-2.0
zzsza/Datascience_School	16. 과최적화와 정규화/02. 교차 검증.ipynb	1	23090	{ "cells": [ { "cell_type": "markdown", "metadata": { "school_cell_uuid": "055ab3178a6042cd8c1b1bed67bfde5b" }, "source": [ "# 교차 검증" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "5cf508ad9cfd4b0cb2e234c79e3cd000" }, "source": [ "## 모형 검증" ] },...	mit
brschneidE3/LegalNetworks	python_code/ipynb/case_class.ipynb	2	8641	{ "cells": [ { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, sys, io\n", "import json\n", "#import simplejson as json\n", "from pprint import pprint\n", "import datetime" ] }, { "cell_type": "c...	mit
ehsteve/ipython-notebooks	Reading HSI Obssum fits .ipynb	2	345801	{ "metadata": { "name": "", "signature": "sha256:812f6911db3213f05b4e1efc3eccc85dc59f4e59303959ea9c767ff1ffdf7fd4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "py...	bsd-3-clause
phuongxuanpham/SelfDrivingCar	CarND-Camera-Calibration/camera_calibration.ipynb	1	875797	{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style> code {background-color : pink !important;} </style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ...	gpl-3.0
Roger-luo/QuDynamics.jl	examples/notebooks/JC_Model_QuTiP_in_QuDynamics_MCWF_method.ipynb	1	37255	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "require(\"/home/amit/Downloads/SemVII/QuBase.jl/src/QuBase.jl\")\n", "using QuBase\n", "require(\"/home/amit/Downloads/Se...	mit
Ledoux/ShareYourSystem	Pythonlogy/draft/Directer/Readme.ipynb	1	8380	{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n<!--\nFrozenIsBool False\n-->\n\n#Directer\n\n##Doc\n----\n\n\n> \n> The Directer is a walker through the folders of the harddrive, \n> assuring a call of _DirectingCallbackFunction at each level.\n> \n> \n\n----\n\n<small>\nV...	mit
paris-saclay-cds/python-workshop	Day_2_Software_engineering_best_practices/solutions/03_code_style.ipynb	1	473748	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import itertools\n", "\n", "import six\n", "\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "%matpl...	bsd-3-clause
dsavoiu/kafe2	examples/jupyter_tutorial_de.ipynb	1	76203	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# Jupyter-Notebook-Tutorial: \n", "# Anpassung von Modellen an Daten mit kafe2 \n", "\n", " Johannes Gäßler, März 2021\n", " ...	gpl-3.0
probml/pyprobml	notebooks/book1/20/fig_20_37.ipynb	1	553	{ "cells": [ { "cell_type": "markdown", "id": "010480a7", "metadata": {}, "source": [ "LLE applied to (a) Swiss roll. Generated by [manifold_swiss_sklearn.ipynb](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/20/manifold_swiss_sklearn.ipynb) . (b) UCI digits. Gen...	mit
NAU-CFL/Python_Learning_Source	02_Setting_Up.ipynb	1	84564	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting Up Python Environment\n", "\n", "Setting up Python environment consists of 4 main elements\n", "\n", "1. Install Python Environment and Necessary Tools\n", "2. Execute Python Commands\n", "3. Run Samp...	mit
luctrudeau/Teaching	IntroductionIOAsync/IntroductionIOAsync.ipynb	1	213534	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introduction à l'I/O Asynchrone" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Le Soc...	lgpl-3.0
ThunderShiviah/code_guild	interactive-coding-challenges/graphs_trees/check_balance/check_balance_challenge.ipynb	2	4276	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<small><i>This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).</i></small>" ] }, { "cell_type": "m...	mit
NeuPhysics/codebase	ipynb/matter/.ipynb_checkpoints/stimulated-checkpoint.ipynb	1	331449	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Stimulated Oscillation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define functions and classes" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": ...	mit
trangel/Data-Science	deep_learning_ai/Tensorflow+Tutorial+dropout.ipynb	1	96963	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Tutorial\n", "\n", "Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build n...	gpl-3.0
anthonyng2/FX-Trading-with-Python-and-Oanda	Oanda v1 REST-oandapy/04.00 Order Management.ipynb	1	11756	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<!--NAVIGATION-->\n", "< [Account Information](03.00 Account Information.ipynb) \| [Contents](Index.ipynb) \| [Trade Management](05.00 Trade Management.ipynb) >" ] }, { "cell_type": "markdown", "metadata": {}, "source...	mit
alexandrejaguar/strata-sv-2015-tutorial	resources/Running the Notebook Server.ipynb	3	14625	{ "metadata": { "name": "", "signature": "sha256:ee4b22b4c949fe21b3e5cda24f0916ba59d8c09443f4a897d98b96d4a73ac335" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Running the Notebook Serve...	bsd-3-clause
justanr/notebooks	hexagonal/refactoring_and_interfaces.ipynb	1	25224	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "I've been thinking a lot about software achitecure lately. Not just thinking, because I wouldn't come up with these ideas on my own, but consuming a lot about it -- books, talks, slide decks, blog posts. And while thinking about all th...	mit
nicolas998/Analisis_Datos	02_Medidas_Localizacion.ipynb	1	338351	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Medidas de Localización " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Medidas típ...	gpl-3.0
Yuanyuan-Shi/batterydeg	.ipynb_checkpoints/phy2nn_battery-checkpoint.ipynb	1	36414	{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.pipeline import Pipeline\n", "from sklearn import datasets, linear_...	mit
HUDataScience/StatisticalMethods2016	notebooks/Basic_Python.ipynb	1	60389	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic of Python.\n", "\n", "The library we are going to use are the following:\n", "\n", "\n", "* numpy " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }...	apache-2.0
afeiguin/comp-phys	14_02_multilayer-networks.ipynb	1	194205	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## How a regression network is traditionally trained\n", "\n", "This network is trained using a data set $D = ({{\\bf x}^{(n)}, {\\bf t}^{(n)}})$ by adjusting ${\\bf w}$ so as to minimize an error func...	mit
JudoWill/ResearchNotebooks	HIVTransTool.ipynb	1	50009	{ "metadata": { "name": "HIVTransTool" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from Bio import Seq\n", "from Bio import SeqIO\n", "import pandas as pd\n", "import numpy as np\n", ...	mit
hoh/Hubbub	notebooks/Analysis-ANN.ipynb	1	28236	{ "metadata": { "name": "", "signature": "sha256:5087d4cc9635b7fad9d8507a6fb11981c40db3a17678c6ccaeacdcb010b90212" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import HTML\n", ...	agpl-3.0
gengho/Car2know	Car2know/analysis/.ipynb_checkpoints/draw_map-checkpoint.ipynb	1	5812796	null	mit
banbh/little-pythoner	python/Main.ipynb	1	12235	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "On this page you'll find a series of exercises. We'll be using Python for all the code, but not really. You barely need to know any Python at all. In fact here is all you need to know (at least about Python).\n", "\n", "## A...	apache-2.0
davisincubator/seal_the_deal	notebooks/kaf_amp_4.0_copy_slow_and_steady_wins_the_race.ipynb	1	20062	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n"...	mit
mmagnus/rna-pdb-tools	notes/rp18-191116.ipynb	2	17780	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# RNA-Puzzle 18" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Init the library and needed functions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [...	gpl-3.0
ajhalthor/statistical-learning-with-R	Chapter 2/notebooks/question 8.ipynb	1	1047184	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "8 (a) We placed the _College.csv_ file in the _Datasets_ directory. Let us access this file." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "d...	mit
ChadFulton/statsmodels	examples/notebooks/generic_mle.ipynb	1	13574	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Maximum Likelihood Estimation (Generic models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give ...	bsd-3-clause
enchantner/python-zero	lesson_9/Slides.ipynb	1	23939	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/html": [ "<style>\n", ".text_cell_render * {\n", " font-family: OfficinaSansCTT;\n", "}\n", "...	mit
aattaran/Machine-Learning-with-Python	titanic/titanic_survival_exploration[1].ipynb	2	97790	{ "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...	bsd-3-clause
luwei0917/awsemmd_script	notebook/Optimization/read_gamma.ipynb	1	3433	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import os\n", "import sys\n", "import random\n", "import time\n", "from random import seed, randint\n", "import argparse\n", "import platform\n", "from datetime import ...	mit
NathanYee/ThinkBayes2	code/report03.ipynb	1	192257	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Report03 - Nathan Yee\n", "\n", "This notebook contains report03 for computational baysian statistics fall 2016\n", "\n", "MIT License: https://opensource.org/licenses/MIT" ] }, { "cell_type": "code", "ex...	gpl-2.0
marxav/hello-world	keras_regression.ipynb	1	191207	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Example of Linear Regression with Keras and TensorFlow\n", "# Y = 10 * X +100" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "n...	mit
swartn/weather-with-pandas	.ipynb_checkpoints/pandas_intro-checkpoint.ipynb	1	114378	{ "metadata": { "name": "", "signature": "sha256:53eb410cc446eb137ff6a3c1f9d06a6eb602d14cbad1fb72f5f1c3b8232bf889" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Weather with Pandas\n", "__neil.ca...	gpl-3.0
ledeprogram/algorithms	class5/homework/najmabadi_shannon_5_1.ipynb	1	67057	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Assignment 1\n", "\n", "Use the data from heights_weights_genders.csv to create a simple predictor that takes in a person's height and guesses their weight based on a model using all the data, regardless of gender. To do th...	gpl-3.0
2php/CodeToolKit	9.caffe-ssd/examples/convert_model.ipynb	1	1730512	null	mit
rohinkumar/AngDiameterTest	Diff_DA_LCDM_LC_z_0.57.ipynb	1	193930	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import scipy as sp\n", "from scipy import integrate\n", "from math import *\n", "impo...	gpl-2.0
DavidBrear/sklearn-cookbook	Chapter 3/3.8 Using KMeans for Outlier Detection.ipynb	1	61538	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Us...	apache-2.0
mtmarsh2/vislab	image_style_experiments/pascal results.ipynb	4	377557	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2\n", "import re\n", "import pandas as pd\n", "import aphrodite.results...	bsd-2-clause
GoogleCloudPlatform/asl-ml-immersion	notebooks/image_models/solutions/2_mnist_models.ipynb	1	20117	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST Image Classification with TensorFlow on Cloud AI Platform\n", "\n", "This notebook demonstrates how to implement different image models on MNIST using the [tf.keras API](https://www.tensorflow.org/versions/r2.0/api_docs...	apache-2.0
FourthCohortAwesome/NightThree	ThreeSoln_jef.ipynb	1	1986	{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter the name of the file: alt2.csv\n" ] } ], "source": [ "import pandas as pd \n", "f...	mit
Wx1ng/Python4DataScience.CH	Series_0_Python_Tutorials/S0EP2_Control_Flow_Data_Structure.ipynb	1	35928	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#1 美观并正确地书写Python语句\n", "\n", "###书写的美观性\n", "\n", "往往问题不仅是美观，还在于程序的可读性：" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "output...	cc0-1.0
lalonica/PhD	vehicles/DataExploration.ipynb	1	166116	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x48170fd0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "...	gpl-3.0
midnighteuler/projecteuler	src/Prob38.ipynb	1	2878	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Take the number 192 and multiply it by each of 1, 2, and 3:\n", "\n", "192 × 1 = 192\n", "\n", "192 × 2 = 384\n", "\n", "192 × 3 = 576\n", "\n", "By concatenating each product we get the 1 to 9 pandigita...	gpl-2.0
jeiros/Jupyter_notebooks	python/markov_analysis/PyEMMA-API.ipynb	1	141652	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing the pyEMMA API" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'2.1'" ] }, "exe...	mit
mdiaz236/DeepLearningFoundations	gan_mnist/Intro_to_GANs_Solution.ipynb	1	179274	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Generative Adversarial Network\n", "\n", "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to ge...	mit
ad960009/dist-keras	examples/example_1_analysis.ipynb	3	49727	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Development and Evaluation\n", "\n", "Joeri Hermans (Technical Student, IT-DB-SAS, CERN) \n", "Departement of Knowledge Engineering \n", "Maastricht University, The Netherlands" ] ...	gpl-3.0

knowledgeanyhow/notebooks

airline/Exploration of Airline On-Time Performance.ipynb

2

375407

{ "metadata": { "name": "", "signature": "sha256:4997000efd69b335ff55f6eed3b9ee20a47bb92f0fd25ec1119af0e633fbe289" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploration of Airline On-Time Performan...

mit

Ledoux/ShareYourSystem

Ouvaton/Concluder.ipynb

1

7082

{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n\n\n#Concluder\n\n##Doc\n----\n\n\n> \n> A Concluder\n> \n> \n\n----\n\n<small>\nView the Concluder notebook on [NbViewer](http://nbviewer.ipython.org/url/shareyoursystem.ouvaton.org/Concluder.ipyn...

mit

NYUDataBootcamp/Projects

UG_F16/RodriguezBallve-Spain's_Labor_Market.ipynb

1

292917

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exploring Spain's Broken Labor Market" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Author** Bosco Rodríguez Ballvé\n", "**Date** Fall 2016\n", "**Class** Data Bootcamp @ NYU Stern \n", ...

mit

tensorflow/probability

tensorflow_probability/python/experimental/nn/examples/vib_dose.ipynb

1

185056

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "XXDeo-aGOAXF" }, "source": [ "##### Copyright 2020 The TensorFlow Authors.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", "executi...

apache-2.0

mne-tools/mne-tools.github.io

dev/_downloads/c6baf7c1a2f53fda44e93271b91f45b8/50_beamformer_lcmv.ipynb

1

15968

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

bsd-3-clause

AaltoML/kalman-jax

kalmanjax/notebooks/2d_log_gaussian_cox_process.ipynb

1

157430

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 2D Log-Gaussian Cox Process via Spatio-Temporal Kalman Smoothing\n", "\n", "## Import and load data" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout...

apache-2.0

4dsolutions/Python5

Comparing JavaScript with Python.ipynb

1

11516

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Python for Everyone!<br/>[Oregon Curriculum Network](http://4dsolutions.net/ocn/)\n", "\n", "## Python and JavaScript\n", "\n", "JavaScript has been moving [a lot closer to Python](http://worldgame.blogspot.com/2016/10/...

mit

WOnder93/pbkdf2-gpu

data/metacentrum/graphs.ipynb

1

680583

{ "metadata": { "name": "", "signature": "sha256:c97123e1d02797e16b0ef122721699efffa69f1c66333f77596b14d6dc9db5a8" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%matplotlib inline\n", "\n", "f...

gpl-2.0

alepoydes/introduction-to-numerical-simulation

practice/What does mean mean mean.ipynb

1

365364

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

mit

yuvrajsingh86/DeepLearning_Udacity

sentiment-network/Sentiment_Classification_Projects.ipynb

1

99598

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n", "\n", "by Andrew Trask\n", "\n", "- **Twitter**: @iamtrask\n", "- **Blog**: http://iamtrask.github.io" ] }, { "cell_type": "m...

mit

mne-tools/mne-tools.github.io

0.15/_downloads/plot_brainstorm_data.ipynb

1

3144

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# Brainstorm tutoria...

bsd-3-clause

hmenke/pairinteraction

doc/sphinx/examples_python/vdw_near_surface.ipynb

3

25578

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Dispersion Coefficients Near Surfaces" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this tutorial we reproduce the results depicted in Figure 5 from J. Block and S. Scheel \"van der Waals intera...

gpl-3.0

xbsd/CS109

content_raj/HW1-Copy0.ipynb

1

37311

{ "metadata": { "name": "", "signature": "sha256:3eabce1a0b9c3e1a9ee432ec43b13290cab7e68011597e38d675bc10224aa3ef" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Homework 1. Which of two things is large...

mit

metpy/MetPy

v0.9/_downloads/ef4bfbf049be071a6c648d7918a50105/Simple_Sounding.ipynb

1

4765

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

bsd-3-clause

intel-analytics/BigDL

apps/ray/parameter_server/sharded_parameter_server.ipynb

1

15424

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# This notebook is adapted from: \n", "https://github.com/ray-project/tutorial/tree/master/examples/sharded_parameter_server.ipynb\n", "\n", "# Sharded Parameter Servers\n", "\n", "**GOAL:** The goal of this exercis...

apache-2.0

Zhang-O/small

tensor__cpu/skimages/get_started.ipynb

2

336126

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import skimage\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "# %matplotlib\n", "%matplotlib inline " ] }, { "cell_type": "code", "execution_count"...

mit

jeroenjanssens/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers

Chapter5_LossFunctions/LossFunctions.ipynb

4

1358823

null

mit

roatienza/Deep-Learning-Experiments

versions/2020/keras/cnn/cnn-siamese.ipynb

1

14604

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Implements a Siamese/Y-Network using Functional API\n", "\n", "~99.4% test accuracy" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output...

mit

root-mirror/training

SummerStudentCourse/2019/Exercises/ROOTBooks/CentralLimitTheorem_Solution.ipynb

5

50466

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Exercise: Central Limit Theorem\n", "\n", "In this exercise we will show what is the Central Limit Theorem and how it applies " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Part 1: Gene...

gpl-2.0

llooker/public-datasets-pipelines

samples/tutorial.ipynb

3

6210

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "7i8lsRFe2lu5" }, "source": [ "# Overview\n", "\n", "Add a brief description of this tutorial here." ] }, { "cell_type": "code", "execution_count": null, "metadata": ...

apache-2.0

stereoboy/Study

Issues/algorithms/Linked Lists.ipynb

1

6428

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Add two numbers represented by linked lists" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "class Node():\n", " def __init__(s...

mit

ocefpaf/folium

examples/plugin-patterns.ipynb

2

137190

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Preleminary demo of the pattern plugin for Folium" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/html": [ "<div style=\"width:100%;\"><div st...

mit

TimothyADavis/KinMSpy

kinms/docs/KinMSpy_tutorial.ipynb

1

227468

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# KinMS galaxy fitting tutorial" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial aims at getting you up and running with galaxy kinematic modelling using _KinMS_! To start you will need to...

mit

napsternxg/ipython-notebooks

Monte Carlo Integration.ipynb

1

232991

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Monte Carlo Integration\n", "\n", "Inspired from the following posts:\n", "\n", "* http://nbviewer.jupyter.org/github/cs109/content/blob/master/labs/lab7/GibbsSampler.ipynb\n", "* http://twiecki.gi...

apache-2.0

mackst/myPyTutorialNotebook

21 类实战(1).ipynb

1

30736

{ "metadata": { "name": "21 \u7c7b\u5b9e\u6218(1)" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "point = Point(2, 3, 4)\n", "point.x # 2\n", "point.y # 3\n", "point.z # 4\n", "\n", ...

mit

Neuroglycerin/neukrill-net-work

notebooks/Checking resumed 40aug and 16aug models.ipynb

1

981797

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using gpu device 2: Tesla K40c\n", ":0: FutureWarning: IPython widgets are experimental and may change i...

mit

linncy/Tester-Automation

cm22c.ipynb

1

2489

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "No Device Found.\n", "Error\n" ] } ], "source": [ "import visa\n", "rm = visa.ResourceManager()\n", "if ...

mit

jimthompson5802/kaggle-BNP-Paribas

src/sandbox/test_model_parms_retrieval.ipynb

1

2616

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/Users/jim/Desktop/Kaggle/BNPParibasCardif/src/sandbox\n" ] } ], "source": [ "import pandas...

mit

jdvelasq/machine-learning

old/ML-00-scraping.ipynb

1

46289

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Aprendizaje de Máquinas -- 0 -- Scraping\n", "Notas de clase sobre aprendizaje de máquinas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Juan David Velásquez Henao** \n", "jdvelasq@unal...

mit

ES-DOC/esdoc-jupyterhub

notebooks/nasa-giss/cmip6/models/giss-e2-1h/aerosol.ipynb

1

84302

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Aerosol \n", "**MIP Era**: CMIP6 \n", "**Institute**: NASA-GISS \n", "**Source ID**: GISS-E2-1H \n", ...

gpl-3.0

ES-DOC/esdoc-jupyterhub

notebooks/cnrm-cerfacs/cmip6/models/cnrm-cm6-1/seaice.ipynb

1

99821

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Seaice \n", "**MIP Era**: CMIP6 \n", "**Institute**: CNRM-CERFACS \n", "**Source ID**: CNRM-CM6-1 \n", ...

gpl-3.0

ledeprogram/algorithms

class9/homework/Kandrach_Sasha_9_3.ipynb

1

4467

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from sklearn import preprocessing\n", "from sklearn import cross_validation\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.feature_...

gpl-3.0

google/starthinker

colabs/cm360_segmentology.ipynb

1

15783

{ "license": "Licensed under the Apache License, Version 2.0", "copyright": "Copyright 2020 Google LLC", "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "CM360 Segmentology", "provenance": [], "collapsed_sections": [], "toc_visible": true }, "kernelspec...

apache-2.0

aborgher/Main-useful-functions-for-ML

.ipynb_checkpoints/NLP-checkpoint.ipynb

1

67248

{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Correction-with-enchant\" data-toc-modified-id=\"Correction-with-enchant-1\"><span class=\"toc-item-num\">1  </span>Correction wit...

gpl-3.0

ES-DOC/esdoc-jupyterhub

notebooks/nerc/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**: NERC \n", "**Source ID**: UKESM1-0-LL \n", "**T...

gpl-3.0

qinwf-nuan/keras-js

notebooks/pipeline/pipeline_10.ipynb

1

27682

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import json\n"...

mit

google-research/google-research

prime/prime_colab.ipynb

1

109093

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "PA8SSKskdEyX" }, "source": [ "\n", "Copyright 2022 Google LLC.\n", "\n", "Licensed under the Apache License, Version 2.0 (the \"License\");" ] }, { "cell_type": "code", ...

apache-2.0

UCBerkeleySETI/blml

gbt_cluster/simple_feature_clustering.ipynb

1

9712776

null

mit

FourthCohortAwesome/NightThree

Night3_DTK.ipynb

1

4446

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Similar type issue from last weeks problem\n", "3 File protocols now\n", "\n", "Protocol1 = 7Col x 3Row --> Alt1\n", "Protocol2 = 2Col x 9Row --> Alt2\n", "Protocol3 = 4Col x 7Row --> Alt3\n", "\n", "File ro...

mit

asimihsan/pydata-ldn2014-writeup

00 - Summary, Highlights, Readings and Order.ipynb

1

20180

{ "metadata": { "name": "", "signature": "sha256:2c00f0ddb7215d7c4cea19200a00bf8d1c743b2cbb0d2fadb582c83fab517cbf" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Intro\n", "\n", "Below is:\...

mit

minh5/cpsc

reports/neiss.ipynb

1

37331

{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev2 toc-item\"><a href=\"#Are-there-products-we-should-be-aware-of?\" data-toc-modified-id=\"Are-there-products-we-should-be-aware-of?-01\"><span class=\"toc-item-num...

mit

JAmarel/Phys202

Integration/IntegrationEx02.ipynb

2

11908

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Integration Exercise 2" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 2, "met...

mit

ES-DOC/esdoc-jupyterhub

notebooks/inm/cmip6/models/sandbox-2/ocean.ipynb

1

164407

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

gpl-3.0

mmckerns/tutmom

solutions.ipynb

2

16237

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Solutions to exercises" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "**EXERCISE:** Solve the constrained programming problem by any of the means above.\n", "\n", "...

bsd-3-clause

williamdjones/protein_binding

notebooks/Neural Network (In Progress).ipynb

1

17477

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import time\n", "import glob\n", "...

mit

Krastanov/cutiepy

examples/Lindblad_Master_Equation_Solver_Examples.ipynb

1

108040

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Table of Contents\n", "\n", "1. [Rabi Oscillations](#Rabi-Oscillations)\n", " 1. [Simulating the Full Hamiltonian](#Simulating-the-Full-Hamiltonian)\n", " 2. [With Rotating Wave Approximation](#With-Rotating-W...

bsd-3-clause

Gorgel/minkpy

analysis/notebooks/V3_analyser_xibox.ipynb

1

270297

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import division\n", "import numpy as np\n", "import scipy as sp\n", "import matplotlib.pyplot as plt\n", "import pylab\n", "from scipy...

gpl-2.0

zzsza/Datascience_School

도커 Tip - 컨테이너의 파일 백업.ipynb

1

6913

{ "cells": [ { "cell_type": "markdown", "metadata": { "school_cell_uuid": "da230397189449d98d3f2f9b8d3da5ae" }, "source": [ "# 도커 Tip - 컨테이너의 파일 백업" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "ba6f3bd3fa4e40b3846186c8a6019abf" }, "source": [ "도커 이...

mit

TANAV/predictorsAndDB

predictorNotebooks/SGDClassifier_Jewelry.ipynb

1

12531

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import cPickle\n", "from scipy.io import loadmat\n", "from sklearn.linear_model import SGDClassifier\n", "from skle...

apache-2.0

albahnsen/ML_SecurityInformatics

notebooks/14-KaggleCompetition.ipynb

1

31809

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 14 - Kaggle Competition\n", "# Fraud Detection\n", "\n", "\n", "## https://inclass.kaggle.com/c/easy-ml-class\n", "\n", "by [Alejandro Correa Bahnsen](albahnsen.com/)\n", "\n", "version 0.1, May 2016\n...

mit

GoogleCloudPlatform/vertex-ai-samples

notebooks/official/pipelines/google_cloud_pipeline_components_automl_text.ipynb

1

33506

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "#...

apache-2.0

lasersonlab/pepsyn

workflows/metagenomic_shotgun_single_end.ipynb

1

231979

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Shotgun metagenomic single end library" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from os.path import join as pjoin\n", "imp...

apache-2.0

zzsza/Datascience_School

16. 과최적화와 정규화/02. 교차 검증.ipynb

1

23090

{ "cells": [ { "cell_type": "markdown", "metadata": { "school_cell_uuid": "055ab3178a6042cd8c1b1bed67bfde5b" }, "source": [ "# 교차 검증" ] }, { "cell_type": "markdown", "metadata": { "school_cell_uuid": "5cf508ad9cfd4b0cb2e234c79e3cd000" }, "source": [ "## 모형 검증" ] },...

mit

brschneidE3/LegalNetworks

python_code/ipynb/case_class.ipynb

2

8641

{ "cells": [ { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os, sys, io\n", "import json\n", "#import simplejson as json\n", "from pprint import pprint\n", "import datetime" ] }, { "cell_type": "c...

mit

ehsteve/ipython-notebooks

Reading HSI Obssum fits .ipynb

2

345801

{ "metadata": { "name": "", "signature": "sha256:812f6911db3213f05b4e1efc3eccc85dc59f4e59303959ea9c767ff1ffdf7fd4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%pylab inline" ], "language": "py...

bsd-3-clause

phuongxuanpham/SelfDrivingCar

CarND-Camera-Calibration/camera_calibration.ipynb

1

875797

{ "cells": [ { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "<style> code {background-color : pink !important;} </style>" ], "text/plain": [ "<IPython.core.display.HTML object>" ...

gpl-3.0

Roger-luo/QuDynamics.jl

examples/notebooks/JC_Model_QuTiP_in_QuDynamics_MCWF_method.ipynb

1

37255

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "require(\"/home/amit/Downloads/SemVII/QuBase.jl/src/QuBase.jl\")\n", "using QuBase\n", "require(\"/home/amit/Downloads/Se...

mit

Ledoux/ShareYourSystem

Pythonlogy/draft/Directer/Readme.ipynb

1

8380

{ "nbformat": 3, "worksheets": [ { "cells": [ { "source": "\n\n\n#Directer\n\n##Doc\n----\n\n\n> \n> The Directer is a walker through the folders of the harddrive, \n> assuring a call of _DirectingCallbackFunction at each level.\n> \n> \n\n----\n\n<small>\nV...

mit

paris-saclay-cds/python-workshop

Day_2_Software_engineering_best_practices/solutions/03_code_style.ipynb

1

473748

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

bsd-3-clause

dsavoiu/kafe2

examples/jupyter_tutorial_de.ipynb

1

76203

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "---\n", "# Jupyter-Notebook-Tutorial: \n", "# Anpassung von Modellen an Daten mit *kafe2* \n", "\n", " Johannes Gäßler, März 2021\n", " ...

gpl-3.0

probml/pyprobml

notebooks/book1/20/fig_20_37.ipynb

1

553

{ "cells": [ { "cell_type": "markdown", "id": "010480a7", "metadata": {}, "source": [ "LLE applied to (a) Swiss roll. Generated by [manifold_swiss_sklearn.ipynb](https://colab.research.google.com/github/probml/pyprobml/blob/master/notebooks/book1/20/manifold_swiss_sklearn.ipynb) . (b) UCI digits. Gen...

mit

NAU-CFL/Python_Learning_Source

02_Setting_Up.ipynb

1

84564

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Setting Up Python Environment\n", "\n", "Setting up Python environment consists of 4 main elements\n", "\n", "1. Install Python Environment and Necessary Tools\n", "2. Execute Python Commands\n", "3. Run Samp...

mit

luctrudeau/Teaching

IntroductionIOAsync/IntroductionIOAsync.ipynb

1

213534

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introduction à l'I/O Asynchrone" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "#Le Soc...

lgpl-3.0

ThunderShiviah/code_guild

interactive-coding-challenges/graphs_trees/check_balance/check_balance_challenge.ipynb

2

4276

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<small><i>This notebook was prepared by [Donne Martin](https://github.com/donnemartin). Source and license info is on [GitHub](https://github.com/donnemartin/interactive-coding-challenges).</i></small>" ] }, { "cell_type": "m...

mit

NeuPhysics/codebase

ipynb/matter/.ipynb_checkpoints/stimulated-checkpoint.ipynb

1

331449

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Stimulated Oscillation" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define functions and classes" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": ...

mit

trangel/Data-Science

deep_learning_ai/Tensorflow+Tutorial+dropout.ipynb

1

96963

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Tutorial\n", "\n", "Welcome to this week's programming assignment. Until now, you've always used numpy to build neural networks. Now we will step you through a deep learning framework that will allow you to build n...

gpl-3.0

anthonyng2/FX-Trading-with-Python-and-Oanda

Oanda v1 REST-oandapy/04.00 Order Management.ipynb

1

11756

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "< [Account Information](03.00 Account Information.ipynb) | [Contents](Index.ipynb) | [Trade Management](05.00 Trade Management.ipynb) >" ] }, { "cell_type": "markdown", "metadata": {}, "source...

mit

alexandrejaguar/strata-sv-2015-tutorial

resources/Running the Notebook Server.ipynb

3

14625

{ "metadata": { "name": "", "signature": "sha256:ee4b22b4c949fe21b3e5cda24f0916ba59d8c09443f4a897d98b96d4a73ac335" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "Running the Notebook Serve...

bsd-3-clause

justanr/notebooks

hexagonal/refactoring_and_interfaces.ipynb

1

25224

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "I've been thinking a lot about software achitecure lately. Not just thinking, because I wouldn't come up with these ideas on my own, but consuming a lot about it -- books, talks, slide decks, blog posts. And while thinking about all th...

mit

nicolas998/Analisis_Datos

02_Medidas_Localizacion.ipynb

1

338351

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Medidas de Localización " ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "fragment" } }, "source": [ "Medidas típ...

gpl-3.0

Yuanyuan-Shi/batterydeg

.ipynb_checkpoints/phy2nn_battery-checkpoint.ipynb

1

36414

{ "cells": [ { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import tensorflow as tf\n", "import matplotlib.pyplot as plt\n", "\n", "from sklearn.pipeline import Pipeline\n", "from sklearn import datasets, linear_...

mit

HUDataScience/StatisticalMethods2016

notebooks/Basic_Python.ipynb

1

60389

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Basic of Python.\n", "\n", "The library we are going to use are the following:\n", "\n", "\n", "* numpy " ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }...

apache-2.0

afeiguin/comp-phys

14_02_multilayer-networks.ipynb

1

194205

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "## How a regression network is traditionally trained\n", "\n", "This network is trained using a data set $D = ({{\\bf x}^{(n)}, {\\bf t}^{(n)}})$ by adjusting ${\\bf w}$ so as to minimize an error func...

mit

JudoWill/ResearchNotebooks

HIVTransTool.ipynb

1

50009

{ "metadata": { "name": "HIVTransTool" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from Bio import Seq\n", "from Bio import SeqIO\n", "import pandas as pd\n", "import numpy as np\n", ...

mit

hoh/Hubbub

notebooks/Analysis-ANN.ipynb

1

28236

{ "metadata": { "name": "", "signature": "sha256:5087d4cc9635b7fad9d8507a6fb11981c40db3a17678c6ccaeacdcb010b90212" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from IPython.display import HTML\n", ...

agpl-3.0

gengho/Car2know

Car2know/analysis/.ipynb_checkpoints/draw_map-checkpoint.ipynb

1

5812796

null

mit

banbh/little-pythoner

python/Main.ipynb

1

12235

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "On this page you'll find a series of exercises. We'll be using Python for all the code, but not really. You barely need to know any Python at all. In fact here is all you need to know (at least about Python).\n", "\n", "## A...

apache-2.0

davisincubator/seal_the_deal

notebooks/kaf_amp_4.0_copy_slow_and_steady_wins_the_race.ipynb

1

20062

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Using TensorFlow backend.\n" ] } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "import os\n"...

mit

mmagnus/rna-pdb-tools

notes/rp18-191116.ipynb

2

17780

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# RNA-Puzzle 18" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Init the library and needed functions." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [...

gpl-3.0

ajhalthor/statistical-learning-with-R

Chapter 2/notebooks/question 8.ipynb

1

1047184

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "**8 (a)** We placed the _College.csv_ file in the _Datasets_ directory. Let us access this file." ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "scrolled": true }, "outputs": [ { "d...

mit

ChadFulton/statsmodels

examples/notebooks/generic_mle.ipynb

1

13574

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Maximum Likelihood Estimation (Generic models)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This tutorial explains how to quickly implement new maximum likelihood models in `statsmodels`. We give ...

bsd-3-clause

enchantner/python-zero

lesson_9/Slides.ipynb

1

23939

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "slideshow": { "slide_type": "skip" } }, "outputs": [ { "data": { "text/html": [ "<style>\n", ".text_cell_render * {\n", " font-family: OfficinaSansCTT;\n", "}\n", "...

mit

aattaran/Machine-Learning-with-Python

titanic/titanic_survival_exploration[1].ipynb

2

97790

{ "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...

bsd-3-clause

luwei0917/awsemmd_script

notebook/Optimization/read_gamma.ipynb

1

3433

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

mit

NathanYee/ThinkBayes2

code/report03.ipynb

1

192257

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Report03 - Nathan Yee\n", "\n", "This notebook contains report03 for computational baysian statistics fall 2016\n", "\n", "MIT License: https://opensource.org/licenses/MIT" ] }, { "cell_type": "code", "ex...

gpl-2.0

marxav/hello-world

keras_regression.ipynb

1

191207

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# Example of Linear Regression with Keras and TensorFlow\n", "# Y = 10 * X +100" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "n...

mit

swartn/weather-with-pandas

.ipynb_checkpoints/pandas_intro-checkpoint.ipynb

1

114378

{ "metadata": { "name": "", "signature": "sha256:53eb410cc446eb137ff6a3c1f9d06a6eb602d14cbad1fb72f5f1c3b8232bf889" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Weather with Pandas\n", "__neil.ca...

gpl-3.0

ledeprogram/algorithms

class5/homework/najmabadi_shannon_5_1.ipynb

1

67057

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Assignment 1\n", "\n", "Use the data from heights_weights_genders.csv to create a simple predictor that takes in a person's height and guesses their weight based on a model using all the data, regardless of gender. To do th...

gpl-3.0

2php/CodeToolKit

9.caffe-ssd/examples/convert_model.ipynb

1

1730512

null

mit

rohinkumar/AngDiameterTest

Diff_DA_LCDM_LC_z_0.57.ipynb

1

193930

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import scipy as sp\n", "from scipy import integrate\n", "from math import *\n", "impo...

gpl-2.0

DavidBrear/sklearn-cookbook

Chapter 3/3.8 Using KMeans for Outlier Detection.ipynb

1

61538

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

apache-2.0

mtmarsh2/vislab

image_style_experiments/pascal results.ipynb

4

377557

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext autoreload\n", "%autoreload 2\n", "import re\n", "import pandas as pd\n", "import aphrodite.results...

bsd-2-clause

GoogleCloudPlatform/asl-ml-immersion

notebooks/image_models/solutions/2_mnist_models.ipynb

1

20117

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# MNIST Image Classification with TensorFlow on Cloud AI Platform\n", "\n", "This notebook demonstrates how to implement different image models on MNIST using the [tf.keras API](https://www.tensorflow.org/versions/r2.0/api_docs...

apache-2.0

FourthCohortAwesome/NightThree

ThreeSoln_jef.ipynb

1

1986

{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Enter the name of the file: alt2.csv\n" ] } ], "source": [ "import pandas as pd \n", "f...

mit

Wx1ng/Python4DataScience.CH

Series_0_Python_Tutorials/S0EP2_Control_Flow_Data_Structure.ipynb

1

35928

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "#1 美观并正确地书写Python语句\n", "\n", "###书写的美观性\n", "\n", "往往问题不仅是美观，还在于程序的可读性：" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "output...

cc0-1.0

lalonica/PhD

vehicles/DataExploration.ipynb

1

166116

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "<matplotlib.axes._subplots.AxesSubplot at 0x48170fd0>" ] }, "execution_count": 5, "metadata": {}, "output_type": "...

gpl-3.0

midnighteuler/projecteuler

src/Prob38.ipynb

1

2878

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Take the number 192 and multiply it by each of 1, 2, and 3:\n", "\n", "192 × 1 = 192\n", "\n", "192 × 2 = 384\n", "\n", "192 × 3 = 576\n", "\n", "By concatenating each product we get the 1 to 9 pandigita...

gpl-2.0

jeiros/Jupyter_notebooks

python/markov_analysis/PyEMMA-API.ipynb

1

141652

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Testing the pyEMMA API" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'2.1'" ] }, "exe...

mit

mdiaz236/DeepLearningFoundations

gan_mnist/Intro_to_GANs_Solution.ipynb

1

179274

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Generative Adversarial Network\n", "\n", "In this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to ge...

mit

ad960009/dist-keras

examples/example_1_analysis.ipynb

3

49727

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Model Development and Evaluation\n", "\n", "**Joeri Hermans** (Technical Student, IT-DB-SAS, CERN) \n", "*Departement of Knowledge Engineering* \n", "*Maastricht University, The Netherlands*" ] ...

gpl-3.0