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
rfinn/capella	matchNSAtoAGC.ipynb	1	19591	{ "metadata": { "name": "", "signature": "sha256:bf5e6f81415adf5b2a6d8118f874313ca1b31599ceff9832a7b3b968d7bf7cd3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of this program is to match the N...	gpl-2.0
palandatarxcom/sklearn_tutorial_cn	notebooks/02.1-Machine-Learning-Intro.ipynb	1	238658	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "这个分析笔记由[Jake Vanderplas](http://www.vanderplas.com)编辑汇总。源代码和license文件在[GitHub](https://github.com/jakevdp/sklearn_tutorial/)。中文翻译由[派兰数据](http://datarx.cn)在[派兰大数据分析平台](http://www.palandata.com)上完成。源代码在[GitHub](https://github.com/pala...	bsd-3-clause
szitenberg/ReproPhyloVagrant	notebooks/Tutorials/Basic/3.8 Building a supermatrix.ipynb	1	31791	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This section shows how to build a supermatrix by providing minimal requirements for gene content per taxon (OTU). This approach is more suited for small scale analysis, because it relies on manual decisions, whereas large scale sup...	mit
XiaowenLin/cs598rk	scripts/red_opal.py.ipynb	1	44037	{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark import SparkContext\n", "from nltk.tokenize import word_tokenize\n", "from nltk.stem import WordNetLemmatizer\n", "import json\n", "import ...	mit
ML4DS/ML4all	R2.kNN_Regression/regression_knn_student.ipynb	1	32110	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "# *The k-nearest neighbors (kNN) regression algorithm*\n", "\n", " Author: Jerónimo Arenas García (jarenas@tsc.uc3m.es)\n", " Jesús Cid S...	mit
james-prior/cohpy	20141207-dojo-dictionary-stuff.ipynb	1	2492	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{2.718281828: 'world', 'hello': 3.1415926}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_res...	mit
TANAV/predictorsAndDB	predictorNotebooks/SGDClassifier_Arts.ipynb	1	12815	{ "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
UCIDataScienceInitiative/IntroToJulia	Notebooks/Clustering.ipynb	2	12263	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Clustering\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Clustering Task\n", "\n", "Use the following dataset:" ] }, { "cell_type": "code", "execution_count": 24, ...	mit
babebe/Yummly	BB/BB_DataCollection-2.ipynb	1	13873	{ "cells": [ { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from yummly import Client\n", "import json\n", "import requests\n", "import pandas as pd\n", "import numpy as np \n", "import re" ] }, { "c...	mit
metpy/MetPy	v1.1/_downloads/5f6dfc4b913dc349eba9f04f6161b5f1/GINI_Water_Vapor.ipynb	1	3223	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# GINI Water V...	bsd-3-clause
pmorissette/bt	examples/PTE.ipynb	1	227540	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import ffn\n", "\n", "#using this import until pip is updated to have the versio...	mit
SlipknotTN/udacity-deeplearning-nanodegree	language-translation/dlnd_language_translation.ipynb	1	1177502	null	mit
wdbm/Psychedelic_Machine_Learning_in_the_Cenozoic_Era	Keras_CNN_newsgroups_text_classification.ipynb	1	117405	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 20 newsgroups classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we use the [20 newsgroups text dataset by Ken Lang](http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/new...	gpl-3.0
vravishankar/Jupyter-Books	Classes+and+Objects.ipynb	1	43697	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Object Oriented Programming" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to Wikipedia, \"Object-oriented programming (OOP) is a programming paradigm based on the concept of 'objects', wh...	mit
ToqueWillot/M2DAC	FDMS/TME2/Modèle linéaire régularisé L1.ipynb	1	85054	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## FDMS TME2\n", "Florian Toque & Paul Willot\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import ...	gpl-2.0
marcolivierarsenault/AdventOfCode2016	06/Day6.ipynb	1	3582	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advent of code Day 6\n", "Look for the most/Least used Char in the messages\n", "Ref: http://adventofcode.com/2016/day/6" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false ...	mit
trangel/Insight-Data-Science	scrapping/scrapping-medhelp.ipynb	2	20162	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests # to make GET request\n", "from bs4 import BeautifulSoup # to parse the HTML response\n", "import time # to pause between calls\n", "import...	gpl-3.0
rubensfernando/mba-analytics-big-data	Python/2016-08-05/aula6-parte1-post.ipynb	1	3136	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Postando uma mensagem simples!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import facebook" ] }, ...	mit
drcjar/pypf	.ipynb_checkpoints/pypf-checkpoint.ipynb	1	28449	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import xlrd \n", "import numpy as np" ], "language": "python", "metadata": {}, ...	agpl-3.0
grezesf/Research	Fun/Costly-Search-Task.ipynb	1	392805	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Task: Guess an integer between 0-X\n", "# guessing the number v costs v\n", "# after each guess we are told if the answer is bigger or smaller\n", "# goal:...	mit
shareactorIO/pipeline	oreilly.ml/high-performance-tensorflow/notebooks/06_Train_Model_XLA_JIT.ipynb	1	2178	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Train Model with XLA JIT Enabled" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the Next Cell to Show XLA JIT Training Code" ] }, { "cell_type": "code", "execution_count": null, ...	apache-2.0
serbyy/MozDef	examples/alerts/AlertDevelopment.ipynb	9	6956	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "#Step one: \n", "#Copy the 'lib' directory from the alerts directory in the mozdef github repo into the directory with this\n",...	mpl-2.0
daniestevez/jupyter_notebooks	Tianwen/orbit/Project Pluto comparison.ipynb	1	48277	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.dates import DateFormatter\n", "from astropy.coordinates import Angle\n", "...	gpl-3.0
jseabold/statsmodels	examples/notebooks/formulas.ipynb	3	9821	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Formulas: Fitting models using R-style formulas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Int...	bsd-3-clause
prisae/blog-notebooks	MX_BarrancasDelCobre.ipynb	1	826144	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Barrancas Del Cobre\n", "\n", "Maps for <https://mexico.werthmuller.org/besucherreisen/barrancasdelcobre>.\n", "\n", "You can find more explanatory examples in Travel.ipynb, also in this dire...	cc0-1.0
nkmk/python-snippets	notebook/sklearn_load_iris.ipynb	1	12640	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.datasets import load_iris" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = ...	mit
diego0020/tutorial-vtk-python	color_map_med.ipynb	1	605584	{ "metadata": { "name": "", "signature": "sha256:5e6d54894543038bc917d2d46b3591b989520083697b36302adf0ebb65cac835" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Processing example 1: Colormaps in 2d Da...	mit
mne-tools/mne-tools.github.io	0.22/_downloads/f781cba191074d5f4243e5933c1e870d/plot_find_ref_artifacts.ipynb	1	7888	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n\n# Find MEG r...	bsd-3-clause
tcstewar/testing_notebooks	Accuracy-Integrator.ipynb	1	54031	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import nengo\n", "import numpy as np\n", "import scipy.optimize\n", "\n", "def accuracy(n_neurons=100, dimens...	gpl-2.0
caganze/wisps	notebooks/cands spt unc.ipynb	1	6640	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import splat\n", "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "import wisps\n", "%matplotlib inline" ] }, { "cell_type": "cod...	mit
DEIB-GECO/PyGMQL	examples/notebooks/02a_Mixing_Local_Remote_Processing_SIMPLE.ipynb	1	133044	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interfacing with an external GMQL service: Aggregating the Chip-Seq signal of histone marks on promotorial regions\n", "\n", "In this first application, genes' promoters are extracted from a local dataset and a large set of C...	apache-2.0
theodoregoetz/wernher	sandbox/Flight.ipynb	1	4376	{ "metadata": { "name": "", "signature": "sha256:1c08f6dbea209f2529be2891629e479bbb4df265d8190897ad996b1a32936f2b" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%run -i '../Common.ipynb'\n", "import...	gpl-3.0
changhoonhahn/centralMS	centralms/notebooks/notes_siglogMstar_tduty.ipynb	1	1156	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# plotting $\\sigma_{\\rm log \\mathcal{M}_*}(\\mathcal{M}_h = 10^{12} M_\\odot)$ as a function of $t_{duty}$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": []...	mit
grehujt/SmallPythonProjects	jupyterNotebooks/tutorials/kde.ipynb	1	107341	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt \n", "import seaborn as sns; sns.set() \n", "import numpy as np" ] }, { "cell_type": "code",...	mit
wesleybeckner/afm-miner	.ipynb_checkpoints/CEI-checkpoint.ipynb	1	6508566	null	mit
ajgpitch/qutip-notebooks	examples/qip-noisy-device-simulator.ipynb	1	412807	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Noisy quantum device simulation with QuTiP\n", "\n", "Author: Boxi Li (etamin1201@gmail.com)\n", "\n", "This is the introduction notebook to the deliverable of one of the Google Summer of Code 2019 project (GSoC2019) ...	lgpl-3.0
cesarcontre/Simulacion2017	Modulo3/.ipynb_checkpoints/Clase20_ProgramaciónLineal-checkpoint.ipynb	2	14613	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Programación lineal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Problemas de programación lineal\n", "\n", "De acuerdo a lo visto la clase pasada, un problema de programación lineal...	mit
awjuliani/DeepRL-Agents	Simple-Policy.ipynb	1	8300	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Simple Reinforcement Learning in Tensorflow Part 1: \n", "## The Multi-armed bandit\n", "This tutorial contains a simple example of how to build a policy-gradient based agent th...	mit
agile-geoscience/striplog	docs/tutorial/10_Extract_curves_into_striplogs.ipynb	1	63145	{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Extract curves into striplogs\n", "\n", "Sometimes you'd like to summarize or otherwise extract curve data (e.g. wireline log data) into a striplog (e.g. one that represents formations).\n", "\n", "We'll start by making some fake CSV text \u2014\u00a0we...	apache-2.0
Kyubyong/numpy_exercises	1_Array_creation_routines.ipynb	1	16400	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Array creation routines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ones and zeros" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, ...	mit
Roc-J/Python_data_science	Data_Mining/Local_outlier_factor.ipynb	2	14496	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 局部异常因子方法发现异常点\n", "\n", "局部异常因子(Local Outlier Factor,LOF)也是一种异常检测算法，它对数据实例的局部密度和邻居进行比较，判断这个数据是否属于相似的密度的区域，它适合从那些簇个数未知，簇的密度和大小各不相同的数据中筛选出异常点。 \n", "\n", "从k近邻算法启发来" ] }, { "cell_type": "code", "execution...	apache-2.0
mayankjohri/LetsExplorePython	Section 3 - Machine Learning/Supervised Learning Algorithm/Classification/5. Perceptron.ipynb	2	4859	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Perceptron" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Perceptron is another simple algorithm suitable for large scale learning. By default:\n", "\n", "- It does not require a learnin...	gpl-3.0
zhongdai/learn-scikit	plot_dataframe/scatter_df.ipynb	1	44662	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "A simple example of an animated plot... In 3D!\n", "\"\"\"\n", "%matplotlib inline\n", "from sklearn.datasets import load_iris\n", "impor...	mit
springcoil/Data-Science-45min-Intros	networks-201/network_analysis.ipynb	19	21325	{ "metadata": { "name": "", "signature": "sha256:4a881f8f2271b5d812c8227bcc208ffa4dac518b854efdf599a9466c78d8a377" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Network Analysis--Using Nu...	unlicense
DOREMUS-ANR/recommender	training/training_unneural.ipynb	1	18905	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/pasquale/anaconda3/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.pyt...	mit
napjon/ds-nd	p2-introds/nyc_subway/project.ipynb	1	354727	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NYC Subway contains regular number of ridership across different conditions. It also contains time series. In this analysis, I investigate whe...	mit
cggh/scikit-allel	notebooks/profiling/tables.ipynb	1	43587	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "import numpy as np\n", "import bcolz\n", "import n...	mit
KatiRG/MethaneEmissions	notebooks/regionalStats.ipynb	1	35280	{ "cells": [ { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Script to read regional stats files for BU and TD approach. \n", "#Values for SumSources mean, min, max written manually into index.html\n", "\n", "#Created...	gpl-3.0
rubensfernando/mba-analytics-big-data	Python/2016-07-22/aula2-parte1-funcoes.ipynb	1	13845	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Funções\n", "\n", "* Até agora, vimos diversos tipos de dados, atribuições, comparações e estruturas de controle.\n", "* A ideia da função é dividir para conquistar, onde:\n", " * Um problema é dividido em diversos...	mit
jlapeyre/SJulia.jl	TutorialNotebooks/StackExchange Examples.ipynb	1	10114	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples from Mathematica Stack Exchange\n", "\n", "This notebook contains examples from [Mathematica Stack Exchange](http://mathematica.stackexchange.com/) applied to Symata.\n", "\n", "Disclaimer to avoid any possib...	mit
jasonding1354/ScalaFAQ	type_system/type_bound.ipynb	1	14125	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 类型边界\n", "类型边界是与类型相关的规则，一个变量要匹配一个类型时必须符合这些规则。\n", "\n", "类型边界的两种形式：\n", "- 类型上界（超类型约束，也称为一致性关系）\n", "- 类型下界（子类型约束）\n", "\n", "类型上界是指，某一类型必须是另一种类型的子类型。类型下界表示某类型必须是另一个类型的父类（或该类型本身）。\n", "\n", "**...	mit
jsharpna/DavisSML	lectures/lecture5/lecture5.ipynb	1	298706	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# The Lasso\n", "\n", "## StatML: Lecture 5\n", "\n", "### Prof. James Sharpnack\n", "\n", "- Some content and images are from \"The Elements of Statistica...	mit
allentran/reinforcement-learning	DP/Policy Evaluation.ipynb	1	8343	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "if \"../\" not in sys.path:\n", " sys.path.append(\"../\") \n", "from lib.envs.gridworld import GridworldEnv" ] }, { "cell_type": "...	mit
hh-italian-group/hh-bbtautau	Studies/notebooks/SignalSelectionNNTraining.ipynb	1	18581	{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import re \n", "import os\n", "import tensorflow as tf\n", "import ROOT\n", "\n", "from tensorflow.keras.callbacks import CSVLogger\n", "\n", "import sys\n", "sys....	gpl-2.0
dr-guangtou/hs_galphot	fake/GalSimSersicTest.ipynb	1	175051	{ "metadata": { "name": "", "signature": "sha256:3c54fca562ebf4d83759b61011f7ed2f2da04b3be4fb847a9bebbee7879e53c6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import galsim\n", "import numpy as np...	bsd-3-clause
sdpython/ensae_teaching_cs	_doc/notebooks/td2a_ml/seasonal_timeseries.ipynb	1	457104	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Timeseries\n", "\n", "Ce notebook pr\u00e9sente quelques \u00e9tapes simples pour une s\u00e9rie temporelle. La plupart utilise le module [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html#m...	mit
bbalasub1/glmnet_python	docs/glmnet_vignette.ipynb	1	583123	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Glmnet Vignette (for python)\n", "July 12, 2017\n", "\n", "## Authors\n", "Trevor Hastie, B. J. Balakumar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n",...	gpl-3.0
guangtunbenzhu/BGT-Cosmology	Spectroscopy/archetype/HST-NFL.ipynb	1	1032097	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from importlib import reload\n", "import mathutils\n", "import f...	mit
waltervh/BornAgain-tutorial	old/python/tutorial.ipynb	2	822124	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Python\n", "\n", "## Useful links\n", "\n", " * BornAgain: http://bornagainproject.org\n", " * BornAgain tutorial: https://github.com/scgmlz/BornAgain-tutorial\n", " * Python official tutorial:...	gpl-3.0
PYPIT/PYPIT	doc/nb/LRIS_blue_notes.ipynb	1	140661	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notes on the LRIS Blue reduction" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# imports\n", "sys.path.append(os.path.abspath('/Users/xavier/local/Python...	gpl-3.0
ozorich/phys202-2015-work	assignments/assignment02/ProjectEuler6.ipynb	1	2532	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 6" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "https://projecteuler.net/problem=6\n", "\n", "The sum of the squares of ...	mit
madsenmj/ml-introduction-course	Class03/Class03.ipynb	1	112482	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Class 03\n", "## Big Data Cleaning: Data Transformations\n", "\n", "Although machine learning is the exciting part of this course, most data scientists spend the vast majority of their time doin...	apache-2.0
Hvass-Labs/TensorFlow-Tutorials	13B_Visual_Analysis_MNIST.ipynb	1	296865	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Tutorial #13-B\n", "# Visual Analysis (MNIST)\n", "\n", "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube]...	mit
speed-of-light/pyslider	docs/nb/ground_truth/grouping_pairs.ipynb	1	240685	{ "metadata": { "name": "", "signature": "sha256:79d88803ccd51376dce851fb34a42cacba19bc5387418efc54b9d23a4e1ad702" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from lib.exp.evaluator.base import Evaluato...	agpl-3.0
y2ee201/Deep-Learning-Nanodegree	sentiment_network/Sentiment Classification - How to Best Frame a Problem for a Neural Network (Lesson 5).ipynb	2	419025	{ "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
tavallaie/pypot	samples/notebooks/Controlling a Poppy Creature using SNAP.ipynb	3	17983	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How-To: Control a PoppyCreature using the visual programming language [Snap!](http://snap.berkeley.edu) (a variant of Scratch)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![alt text](image/sna...	gpl-3.0
Startupsci/data-science-notebooks	titanic-data-science-solutions.ipynb	1	261787	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Titanic Data Science Solutions\n", "\n", "This notebook is companion to the book [Data Science Solutions](https://startupsci.com). The notebook walks us through a typical workflow for solving data science competitions at site...	mit
mannyfin/IRAS	Type C calibrations/TypeC calcs corrected.ipynb	1	262654	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# The situation\n", "\n", "Type C thermocouples are not NIST calibrated to below 273.15 K. For my research specific scenario, I need to cool my sample (Molybdenum) to cryogenic temperatures and also an...	bsd-3-clause
sdpython/pymyinstall	_doc/notebooks/example_xgboost.ipynb	1	16622	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# example with xgboost\n", "\n", "Test XGBoost after it was compiled, pickle, unpickle." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs":...	mit
leosartaj/scipy-2016-tutorial	tutorial_exercises/Advanced-Simplification.ipynb	1	13297	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simplification" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import *\n", "x, y, z = symbols('x y z')\n", "init_...	bsd-3-clause
pxcandeias/py-notebooks	FRF_plots.ipynb	1	186155	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='top'></a>\n", "\n", "# Frequency Response Functions (FRFs) plots\n", "\n", "This notebook is about [frequency response functions](http://www.vibrationdata.com/tutorials/frf.pdf) (FRFs) and the various ways they c...	mit
lithiumdenis/MLSchool	2. Бостон.ipynb	1	21381	{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:05:06.359703", "start_time": "2017-07-19T12:05:06.349392" }, "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np...	mit
balouf/INF674	01-Galton-Watson-TP.ipynb	1	21238	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ACN 903 S1: Galton-Watson Process\n", "\n", "## Céline Comte & Fabien Mathieu" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pylab...	gpl-3.0
eecs445-f16/umich-eecs445-f16	handsOn_lecture04_linear-regression-part1/lecture04_linear-regression-part-1.ipynb	1	14699	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "$$ \\LaTeX \\text{ command declarations here.}\n", "\\newcommand{\\R}{\\mathbb{R}}\n", "\\renewcommand{\\vec}[1]{\\mathbf{#1}}\n", "$$" ] }, { "cell_type": "m...	mit
nicolasm/lastfm-notebooks	lastfm-plays.ipynb	1	3063	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import MySQLdb\n", "import netrc\n", "import pandas\n", "import lastfmDb as lf\n", "import datetime\n", "\n", "from plotly import __version__\n...	mit
mlmurray/TensorFlow-Experimentation	notebooks/3 - Neural Networks/convolutional_network.ipynb	1	11286	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# A Convolutional Network implementation example using TensorFlow library.\n", "# This example is using the MNIST database of handwritten digits\n", "# (http://ya...	mit
nkmk/python-snippets	notebook/str_removeprefix_removesuffix.ipynb	1	3748	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "s = 'abc-abcxyz'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ...	mit
PaulSoderlind/FinancialTheoryMSc	Ch17_Bonds2.ipynb	1	59028	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bonds 2\n", "\n", "This notebook discusses duration hedging as a way to immunize a bond portfolio." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Packages and Extra Functions" ]...	mit
ES-DOC/esdoc-jupyterhub	notebooks/bnu/cmip6/models/sandbox-3/land.ipynb	1	173496	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Land \n", "MIP Era: CMIP6 \n", "Institute: BNU \n", "Source ID: SANDBOX-3 \n", "Topic...	gpl-3.0
liuhanfei0615/liupengyuan.github.io	chapter1/homework/computer/笔记+201611680927.md.ipynb	27	2246	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 如何使用jupyter notebook与Github\n", "### jupyter notebook的使用 \n", "\n", "1. python的安装<br> \n", "* 进入http://www.python.org/ftp/python/3.6.0/python-3.6.0-amd64-webinstall.exe<br>\n", "- 安装到D:\\python下(安装过程中勾选 Ins...	mit
DS-100/sp17-materials	sp17/disc/disc11/disc11_solution.ipynb	1	17308	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Discussion 11: Logistic Regression and Gradient Descent" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": tru...	gpl-3.0
KatiRG/flyingpigeon	notebooks/modules_sdm.ipynb	1	6510	{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/nils\n", "['/home/nils/data/sdm/FD_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1...	apache-2.0
girpas-ulg/nb_geoschem	Extract_GCprof_station_dev.ipynb	1	1164322	null	lgpl-3.0
tridesclous/tridesclous_examples	Brochier_motor_cortex_96_channels/Brochier_motor_cortex_96_channels.ipynb	1	110874	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Massively parallel recordings in macaque motor cortex\n", "\n", "Brochier and collaborator release public dataset recorded with 10-by-10 Utah electrode arrays on macaque motor cortex.\n", "\n", "See https://www.nature...	mit
probml/pyprobml	deprecated/simulated_annealing_2d_demo.ipynb	1	543381	{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "simulated-annealing-2d.pynb", "provenance": [], "toc_visible": true, "authorship_tag": "ABX9TyP9S1O4x5pDb8rKiz1Xcsaw", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, ...	mit
mne-tools/mne-tools.github.io	0.20/_downloads/bf3ad991f7c7776e245520709f49cb04/plot_cwt_sensor_connectivity.ipynb	1	4057	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n# Compute seed...	bsd-3-clause
balarsen/pymc_learning	Mixture/Gaussian Mixture.ipynb	1	3040946	null	bsd-3-clause
Tatiana-Krivosheev/ipython-notebooks-physics	Phys2212L.Capacitors.ipynb	1	19576	{ "metadata": { "name": "", "signature": "sha256:c8f331dfcac9cece8daec4ac0ecedd6e63b792cc34a2911afa1d88f646373747" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "PHYS 2212L - Principles of...	cc0-1.0
jorgedominguezchavez/dlnd_first_neural_network	Your_first_neural_network.ipynb	1	312379	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Your first neural network\n", "\n", "In this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural networ...	mit
pysal/pPysal	weights/EvenFasterSerialContiguity.ipynb	1	86431	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import time\n", "import collections\n", "import sys\n", "sys.path.append('/Users/jay/github/pysal/')\n", "imp...	bsd-3-clause
shreyasva/tensorflow	tensorflow/tools/docker/notebooks/2_getting_started.ipynb	3	143937	{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6TuWv0Y0sY8n" }, "source": [ "# Getting Started in TensorFlow\n", "## A look at a very simple neural network in TensorFlow" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text...	apache-2.0
DawesLab/LabNotebooks	Pu Zhang QuTiP List.ipynb	1	23461	{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadat...	mit
jhconning/Dev-II	notebooks/InsecureRights.ipynb	1	20065	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causes and consequences of insecure Property Rights to land\n", "\n", "Insecure property rights to land can affect investement decisions and the nature and efficiency of agrarian production organization by affecting the marke...	bsd-3-clause
jbarratt/ipython_notebook_presentation	Command Line Demo.ipynb	1	3850	{ "metadata": { "name": "", "signature": "sha256:6cf257f44568ee9ffbdcd690881700f54d7b3728e6148a92ae40a2ad8355e55f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IPython Command Line examples\n", ...	cc0-1.0
jskDr/jamespy_py3	wireless/polar_nb/Sage - NPolar Transform-Copy5.ipynb	2	17235	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Symbolic Polar Transformation using Sagemath" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from sage import *\n", "import numpy as np\n", "from wirele...	mit
kkaiser/kkaiser.github.io	social_data/nb/project_assignmentB.ipynb	1	30831883	null	gpl-3.0
jfconavarrete/spts-uoe	build_full_dataset.ipynb	1	30569	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Merge Datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set Up" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [],...	gpl-3.0
anodos-ru/catalog	updaters/comptek.ipynb	1	5030	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comptek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Инициализация" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": ...	mit
GLiCom/CorpusAnalysis2016	notebooks/Practica 8.ipynb	1	3657	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Práctica 8: corpora and collocations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ejercicio: completar el siguiente Notebook. Añadir celdas adicionales para pasos intermedios y **mostrar los resul...	apache-2.0
SiggyF/notebooks	sealevelexample.ipynb	1	497117	{ "metadata": { "name": "", "signature": "sha256:2be34651646d672442140d1fc4158c3fe65f4deb31b9a4c3a74a2136aad3fcdc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Sea-level rise \n", "===============...	gpl-3.0

rfinn/capella

matchNSAtoAGC.ipynb

1

19591

{ "metadata": { "name": "", "signature": "sha256:bf5e6f81415adf5b2a6d8118f874313ca1b31599ceff9832a7b3b968d7bf7cd3" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "The goal of this program is to match the N...

gpl-2.0

palandatarxcom/sklearn_tutorial_cn

notebooks/02.1-Machine-Learning-Intro.ipynb

1

238658

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "这个分析笔记由[Jake Vanderplas](http://www.vanderplas.com)编辑汇总。源代码和license文件在[GitHub](https://github.com/jakevdp/sklearn_tutorial/)。中文翻译由[派兰数据](http://datarx.cn)在[派兰大数据分析平台](http://www.palandata.com)上完成。源代码在[GitHub](https://github.com/pala...

bsd-3-clause

szitenberg/ReproPhyloVagrant

notebooks/Tutorials/Basic/3.8 Building a supermatrix.ipynb

1

31791

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This section shows how to build a supermatrix by providing minimal requirements for gene content per taxon (OTU). This approach is more suited for *small scale* analysis, because it relies on manual decisions, whereas *large scale* sup...

mit

XiaowenLin/cs598rk

scripts/red_opal.py.ipynb

1

44037

{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from pyspark import SparkContext\n", "from nltk.tokenize import word_tokenize\n", "from nltk.stem import WordNetLemmatizer\n", "import json\n", "import ...

mit

ML4DS/ML4all

R2.kNN_Regression/regression_knn_student.ipynb

1

32110

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "\n", "# **The *k*-nearest neighbors (*k*NN) regression algorithm**\n", "\n", " Author: Jerónimo Arenas García (jarenas@tsc.uc3m.es)\n", " Jesús Cid S...

mit

james-prior/cohpy

20141207-dojo-dictionary-stuff.ipynb

1

2492

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{2.718281828: 'world', 'hello': 3.1415926}" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_res...

mit

TANAV/predictorsAndDB

predictorNotebooks/SGDClassifier_Arts.ipynb

1

12815

{ "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

UCIDataScienceInitiative/IntroToJulia

Notebooks/Clustering.ipynb

2

12263

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Clustering\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Clustering Task\n", "\n", "Use the following dataset:" ] }, { "cell_type": "code", "execution_count": 24, ...

mit

babebe/Yummly

BB/BB_DataCollection-2.ipynb

1

13873

{ "cells": [ { "cell_type": "code", "execution_count": 32, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from yummly import Client\n", "import json\n", "import requests\n", "import pandas as pd\n", "import numpy as np \n", "import re" ] }, { "c...

mit

metpy/MetPy

v1.1/_downloads/5f6dfc4b913dc349eba9f04f6161b5f1/GINI_Water_Vapor.ipynb

1

3223

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

bsd-3-clause

pmorissette/bt

examples/PTE.ipynb

1

227540

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "\n", "import ffn\n", "\n", "#using this import until pip is updated to have the versio...

mit

SlipknotTN/udacity-deeplearning-nanodegree

language-translation/dlnd_language_translation.ipynb

1

1177502

null

mit

wdbm/Psychedelic_Machine_Learning_in_the_Cenozoic_Era

Keras_CNN_newsgroups_text_classification.ipynb

1

117405

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# 20 newsgroups classification" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we use the [20 newsgroups text dataset by Ken Lang](http://www.cs.cmu.edu/afs/cs.cmu.edu/project/theo-20/www/data/new...

gpl-3.0

vravishankar/Jupyter-Books

Classes+and+Objects.ipynb

1

43697

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Object Oriented Programming" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "According to Wikipedia, \"Object-oriented programming (OOP) is a programming paradigm based on the concept of 'objects', wh...

mit

ToqueWillot/M2DAC

FDMS/TME2/Modèle linéaire régularisé L1.ipynb

1

85054

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## FDMS TME2\n", "Florian Toque & Paul Willot\n" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import ...

gpl-2.0

marcolivierarsenault/AdventOfCode2016

06/Day6.ipynb

1

3582

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Advent of code Day 6\n", "Look for the most/Least used Char in the messages\n", "Ref: http://adventofcode.com/2016/day/6" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false ...

mit

trangel/Insight-Data-Science

scrapping/scrapping-medhelp.ipynb

2

20162

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import requests # to make GET request\n", "from bs4 import BeautifulSoup # to parse the HTML response\n", "import time # to pause between calls\n", "import...

gpl-3.0

rubensfernando/mba-analytics-big-data

Python/2016-08-05/aula6-parte1-post.ipynb

1

3136

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Postando uma mensagem simples!" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import facebook" ] }, ...

mit

drcjar/pypf

.ipynb_checkpoints/pypf-checkpoint.ipynb

1

28449

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import pandas as pd\n", "import xlrd \n", "import numpy as np" ], "language": "python", "metadata": {}, ...

agpl-3.0

grezesf/Research

Fun/Costly-Search-Task.ipynb

1

392805

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Task: Guess an integer between 0-X\n", "# guessing the number v costs v\n", "# after each guess we are told if the answer is bigger or smaller\n", "# goal:...

mit

shareactorIO/pipeline

oreilly.ml/high-performance-tensorflow/notebooks/06_Train_Model_XLA_JIT.ipynb

1

2178

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Train Model with XLA JIT Enabled" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run the Next Cell to Show XLA JIT Training Code" ] }, { "cell_type": "code", "execution_count": null, ...

apache-2.0

serbyy/MozDef

examples/alerts/AlertDevelopment.ipynb

9

6956

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "#Step one: \n", "#Copy the 'lib' directory from the alerts directory in the mozdef github repo into the directory with this\n",...

mpl-2.0

daniestevez/jupyter_notebooks

Tianwen/orbit/Project Pluto comparison.ipynb

1

48277

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from matplotlib.dates import DateFormatter\n", "from astropy.coordinates import Angle\n", "...

gpl-3.0

jseabold/statsmodels

examples/notebooks/formulas.ipynb

3

9821

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Formulas: Fitting models using R-style formulas" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since version 0.5.0, ``statsmodels`` allows users to fit statistical models using R-style formulas. Int...

bsd-3-clause

prisae/blog-notebooks

MX_BarrancasDelCobre.ipynb

1

826144

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Barrancas Del Cobre\n", "\n", "Maps for <https://mexico.werthmuller.org/besucherreisen/barrancasdelcobre>.\n", "\n", "You can find more explanatory examples in Travel.ipynb, also in this dire...

cc0-1.0

nkmk/python-snippets

notebook/sklearn_load_iris.ipynb

1

12640

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "from sklearn.datasets import load_iris" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data = ...

mit

diego0020/tutorial-vtk-python

color_map_med.ipynb

1

605584

{ "metadata": { "name": "", "signature": "sha256:5e6d54894543038bc917d2d46b3591b989520083697b36302adf0ebb65cac835" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Processing example 1: Colormaps in 2d Da...

mit

mne-tools/mne-tools.github.io

0.22/_downloads/f781cba191074d5f4243e5933c1e870d/plot_find_ref_artifacts.ipynb

1

7888

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

bsd-3-clause

tcstewar/testing_notebooks

Accuracy-Integrator.ipynb

1

54031

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import nengo\n", "import numpy as np\n", "import scipy.optimize\n", "\n", "def accuracy(n_neurons=100, dimens...

gpl-2.0

caganze/wisps

notebooks/cands spt unc.ipynb

1

6640

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

mit

DEIB-GECO/PyGMQL

examples/notebooks/02a_Mixing_Local_Remote_Processing_SIMPLE.ipynb

1

133044

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Interfacing with an external GMQL service: Aggregating the Chip-Seq signal of histone marks on promotorial regions\n", "\n", "In this first application, genes' promoters are extracted from a local dataset and a large set of C...

apache-2.0

theodoregoetz/wernher

sandbox/Flight.ipynb

1

4376

{ "metadata": { "name": "", "signature": "sha256:1c08f6dbea209f2529be2891629e479bbb4df265d8190897ad996b1a32936f2b" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%run -i '../Common.ipynb'\n", "import...

gpl-3.0

changhoonhahn/centralMS

centralms/notebooks/notes_siglogMstar_tduty.ipynb

1

1156

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# plotting $\\sigma_{\\rm log \\mathcal{M}_*}(\\mathcal{M}_h = 10^{12} M_\\odot)$ as a function of $t_{duty}$" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": []...

mit

grehujt/SmallPythonProjects

jupyterNotebooks/tutorials/kde.ipynb

1

107341

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

mit

wesleybeckner/afm-miner

.ipynb_checkpoints/CEI-checkpoint.ipynb

1

6508566

null

mit

ajgpitch/qutip-notebooks

examples/qip-noisy-device-simulator.ipynb

1

412807

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Noisy quantum device simulation with QuTiP\n", "\n", "Author: Boxi Li (etamin1201@gmail.com)\n", "\n", "This is the introduction notebook to the deliverable of one of the Google Summer of Code 2019 project (GSoC2019) ...

lgpl-3.0

cesarcontre/Simulacion2017

Modulo3/.ipynb_checkpoints/Clase20_ProgramaciónLineal-checkpoint.ipynb

2

14613

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Programación lineal" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Problemas de programación lineal\n", "\n", "De acuerdo a lo visto la clase pasada, un problema de programación lineal...

mit

awjuliani/DeepRL-Agents

Simple-Policy.ipynb

1

8300

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Simple Reinforcement Learning in Tensorflow Part 1: \n", "## The Multi-armed bandit\n", "This tutorial contains a simple example of how to build a policy-gradient based agent th...

mit

agile-geoscience/striplog

docs/tutorial/10_Extract_curves_into_striplogs.ipynb

1

63145

{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Extract curves into striplogs\n", "\n", "Sometimes you'd like to summarize or otherwise extract curve data (e.g. wireline log data) into a striplog (e.g. one that represents formations).\n", "\n", "We'll start by making some fake CSV text \u2014\u00a0we...

apache-2.0

Kyubyong/numpy_exercises

1_Array_creation_routines.ipynb

1

16400

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Array creation routines" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Ones and zeros" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, ...

mit

Roc-J/Python_data_science

Data_Mining/Local_outlier_factor.ipynb

2

14496

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 局部异常因子方法发现异常点\n", "\n", "局部异常因子(Local Outlier Factor,LOF)也是一种异常检测算法，它对数据实例的局部密度和邻居进行比较，判断这个数据是否属于相似的密度的区域，它适合从那些簇个数未知，簇的密度和大小各不相同的数据中筛选出异常点。 \n", "\n", "从k近邻算法启发来" ] }, { "cell_type": "code", "execution...

apache-2.0

mayankjohri/LetsExplorePython

Section 3 - Machine Learning/Supervised Learning Algorithm/Classification/5. Perceptron.ipynb

2

4859

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Perceptron" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The Perceptron is another simple algorithm suitable for large scale learning. By default:\n", "\n", "- It does not require a learnin...

gpl-3.0

zhongdai/learn-scikit

plot_dataframe/scatter_df.ipynb

1

44662

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\"\"\"\n", "A simple example of an animated plot... In 3D!\n", "\"\"\"\n", "%matplotlib inline\n", "from sklearn.datasets import load_iris\n", "impor...

mit

springcoil/Data-Science-45min-Intros

networks-201/network_analysis.ipynb

19

21325

{ "metadata": { "name": "", "signature": "sha256:4a881f8f2271b5d812c8227bcc208ffa4dac518b854efdf599a9466c78d8a377" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 2, "metadata": {}, "source": [ "Network Analysis--Using Nu...

unlicense

DOREMUS-ANR/recommender

training/training_unneural.ipynb

1

18905

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/Users/pasquale/anaconda3/lib/python3.5/importlib/_bootstrap.py:222: RuntimeWarning: compiletime version 3.6 of module 'tensorflow.pyt...

mit

napjon/ds-nd

p2-introds/nyc_subway/project.ipynb

1

354727

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Overview" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "NYC Subway contains regular number of ridership across different conditions. It also contains time series. In this analysis, I investigate whe...

mit

cggh/scikit-allel

notebooks/profiling/tables.ipynb

1

43587

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import sys\n", "import numpy as np\n", "import bcolz\n", "import n...

mit

KatiRG/MethaneEmissions

notebooks/regionalStats.ipynb

1

35280

{ "cells": [ { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Script to read regional stats files for BU and TD approach. \n", "#Values for SumSources mean, min, max written manually into index.html\n", "\n", "#Created...

gpl-3.0

rubensfernando/mba-analytics-big-data

Python/2016-07-22/aula2-parte1-funcoes.ipynb

1

13845

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Funções\n", "\n", "* Até agora, vimos diversos tipos de dados, atribuições, comparações e estruturas de controle.\n", "* A ideia da função é dividir para conquistar, onde:\n", " * Um problema é dividido em diversos...

mit

jlapeyre/SJulia.jl

TutorialNotebooks/StackExchange Examples.ipynb

1

10114

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Examples from Mathematica Stack Exchange\n", "\n", "This notebook contains examples from [Mathematica Stack Exchange](http://mathematica.stackexchange.com/) applied to Symata.\n", "\n", "Disclaimer to avoid any possib...

mit

jasonding1354/ScalaFAQ

type_system/type_bound.ipynb

1

14125

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. 类型边界\n", "类型边界是与类型相关的规则，一个变量要匹配一个类型时必须符合这些规则。\n", "\n", "类型边界的两种形式：\n", "- 类型上界（超类型约束，也称为一致性关系）\n", "- 类型下界（子类型约束）\n", "\n", "类型上界是指，某一类型必须是另一种类型的子类型。类型下界表示某类型必须是另一个类型的父类（或该类型本身）。\n", "\n", "**...

mit

jsharpna/DavisSML

lectures/lecture5/lecture5.ipynb

1

298706

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# The Lasso\n", "\n", "## StatML: Lecture 5\n", "\n", "### Prof. James Sharpnack\n", "\n", "- Some content and images are from \"The Elements of Statistica...

mit

allentran/reinforcement-learning

DP/Policy Evaluation.ipynb

1

8343

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import sys\n", "if \"../\" not in sys.path:\n", " sys.path.append(\"../\") \n", "from lib.envs.gridworld import GridworldEnv" ] }, { "cell_type": "...

mit

hh-italian-group/hh-bbtautau

Studies/notebooks/SignalSelectionNNTraining.ipynb

1

18581

{ "cells": [ { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "import re \n", "import os\n", "import tensorflow as tf\n", "import ROOT\n", "\n", "from tensorflow.keras.callbacks import CSVLogger\n", "\n", "import sys\n", "sys....

gpl-2.0

dr-guangtou/hs_galphot

fake/GalSimSersicTest.ipynb

1

175051

{ "metadata": { "name": "", "signature": "sha256:3c54fca562ebf4d83759b61011f7ed2f2da04b3be4fb847a9bebbee7879e53c6" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import galsim\n", "import numpy as np...

bsd-3-clause

sdpython/ensae_teaching_cs

_doc/notebooks/td2a_ml/seasonal_timeseries.ipynb

1

457104

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Timeseries\n", "\n", "Ce notebook pr\u00e9sente quelques \u00e9tapes simples pour une s\u00e9rie temporelle. La plupart utilise le module [statsmodels.tsa](https://www.statsmodels.org/stable/tsa.html#m...

mit

bbalasub1/glmnet_python

docs/glmnet_vignette.ipynb

1

583123

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Glmnet Vignette (for python)\n", "July 12, 2017\n", "\n", "## Authors\n", "Trevor Hastie, B. J. Balakumar" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Introduction\n", "\n",...

gpl-3.0

guangtunbenzhu/BGT-Cosmology

Spectroscopy/archetype/HST-NFL.ipynb

1

1032097

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

mit

waltervh/BornAgain-tutorial

old/python/tutorial.ipynb

2

822124

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introduction to Python\n", "\n", "## Useful links\n", "\n", " * BornAgain: http://bornagainproject.org\n", " * BornAgain tutorial: https://github.com/scgmlz/BornAgain-tutorial\n", " * Python official tutorial:...

gpl-3.0

PYPIT/PYPIT

doc/nb/LRIS_blue_notes.ipynb

1

140661

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Notes on the LRIS Blue reduction" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# imports\n", "sys.path.append(os.path.abspath('/Users/xavier/local/Python...

gpl-3.0

ozorich/phys202-2015-work

assignments/assignment02/ProjectEuler6.ipynb

1

2532

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Project Euler: Problem 6" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "https://projecteuler.net/problem=6\n", "\n", "The sum of the squares of ...

mit

madsenmj/ml-introduction-course

Class03/Class03.ipynb

1

112482

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": false }, "source": [ "# Class 03\n", "## Big Data Cleaning: Data Transformations\n", "\n", "Although machine learning is the exciting part of this course, most data scientists spend the vast majority of their time doin...

apache-2.0

Hvass-Labs/TensorFlow-Tutorials

13B_Visual_Analysis_MNIST.ipynb

1

296865

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# TensorFlow Tutorial #13-B\n", "# Visual Analysis (MNIST)\n", "\n", "by [Magnus Erik Hvass Pedersen](http://www.hvass-labs.org/)\n", "/ [GitHub](https://github.com/Hvass-Labs/TensorFlow-Tutorials) / [Videos on YouTube]...

mit

speed-of-light/pyslider

docs/nb/ground_truth/grouping_pairs.ipynb

1

240685

{ "metadata": { "name": "", "signature": "sha256:79d88803ccd51376dce851fb34a42cacba19bc5387418efc54b9d23a4e1ad702" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from lib.exp.evaluator.base import Evaluato...

agpl-3.0

y2ee201/Deep-Learning-Nanodegree

sentiment_network/Sentiment Classification - How to Best Frame a Problem for a Neural Network (Lesson 5).ipynb

2

419025

{ "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

tavallaie/pypot

samples/notebooks/Controlling a Poppy Creature using SNAP.ipynb

3

17983

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# How-To: Control a PoppyCreature using the visual programming language [Snap!](http://snap.berkeley.edu) *(a variant of Scratch)*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "![alt text](image/sna...

gpl-3.0

Startupsci/data-science-notebooks

titanic-data-science-solutions.ipynb

1

261787

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Titanic Data Science Solutions\n", "\n", "This notebook is companion to the book [Data Science Solutions](https://startupsci.com). The notebook walks us through a typical workflow for solving data science competitions at site...

mit

mannyfin/IRAS

Type C calibrations/TypeC calcs corrected.ipynb

1

262654

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# The situation\n", "\n", "Type C thermocouples are not NIST calibrated to below 273.15 K. For my research specific scenario, I need to cool my sample (Molybdenum) to cryogenic temperatures and also an...

bsd-3-clause

sdpython/pymyinstall

_doc/notebooks/example_xgboost.ipynb

1

16622

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# example with xgboost\n", "\n", "Test XGBoost after it was compiled, pickle, unpickle." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs":...

mit

leosartaj/scipy-2016-tutorial

tutorial_exercises/Advanced-Simplification.ipynb

1

13297

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Simplification" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from sympy import *\n", "x, y, z = symbols('x y z')\n", "init_...

bsd-3-clause

pxcandeias/py-notebooks

FRF_plots.ipynb

1

186155

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<a id='top'></a>\n", "\n", "# Frequency Response Functions (FRFs) plots\n", "\n", "This notebook is about [frequency response functions](http://www.vibrationdata.com/tutorials/frf.pdf) (FRFs) and the various ways they c...

mit

lithiumdenis/MLSchool

2. Бостон.ipynb

1

21381

{ "cells": [ { "cell_type": "code", "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2017-07-19T12:05:06.359703", "start_time": "2017-07-19T12:05:06.349392" }, "collapsed": true }, "outputs": [], "source": [ "import pandas as pd\n", "import numpy as np...

mit

balouf/INF674

01-Galton-Watson-TP.ipynb

1

21238

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# ACN 903 S1: Galton-Watson Process\n", "\n", "## Céline Comte & Fabien Mathieu" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%pylab...

gpl-3.0

eecs445-f16/umich-eecs445-f16

handsOn_lecture04_linear-regression-part1/lecture04_linear-regression-part-1.ipynb

1

14699

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "skip" } }, "source": [ "$$ \\LaTeX \\text{ command declarations here.}\n", "\\newcommand{\\R}{\\mathbb{R}}\n", "\\renewcommand{\\vec}[1]{\\mathbf{#1}}\n", "$$" ] }, { "cell_type": "m...

mit

nicolasm/lastfm-notebooks

lastfm-plays.ipynb

1

3063

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import MySQLdb\n", "import netrc\n", "import pandas\n", "import lastfmDb as lf\n", "import datetime\n", "\n", "from plotly import __version__\n...

mit

mlmurray/TensorFlow-Experimentation

notebooks/3 - Neural Networks/convolutional_network.ipynb

1

11286

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# A Convolutional Network implementation example using TensorFlow library.\n", "# This example is using the MNIST database of handwritten digits\n", "# (http://ya...

mit

nkmk/python-snippets

notebook/str_removeprefix_removesuffix.ipynb

1

3748

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "s = 'abc-abcxyz'" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ ...

mit

PaulSoderlind/FinancialTheoryMSc

Ch17_Bonds2.ipynb

1

59028

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Bonds 2\n", "\n", "This notebook discusses *duration hedging* as a way to immunize a bond portfolio." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load Packages and Extra Functions" ]...

mit

ES-DOC/esdoc-jupyterhub

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

1

173496

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

gpl-3.0

liuhanfei0615/liupengyuan.github.io

chapter1/homework/computer/笔记+201611680927.md.ipynb

27

2246

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## 如何使用jupyter notebook与Github\n", "### jupyter notebook的使用 \n", "\n", "**1. python的安装**<br> \n", "* 进入http://www.python.org/ftp/python/3.6.0/python-3.6.0-amd64-webinstall.exe<br>\n", "- 安装到D:\\python下(安装过程中勾选 Ins...

mit

DS-100/sp17-materials

sp17/disc/disc11/disc11_solution.ipynb

1

17308

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "## Discussion 11: Logistic Regression and Gradient Descent" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "deletable": tru...

gpl-3.0

KatiRG/flyingpigeon

notebooks/modules_sdm.ipynb

1

6510

{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "/home/nils\n", "['/home/nils/data/sdm/FD_EUR-44_MPI-M-MPI-ESM-LR_historical_r1i1p1_CLMcom-CCLM4-8-17_v1...

apache-2.0

girpas-ulg/nb_geoschem

Extract_GCprof_station_dev.ipynb

1

1164322

null

lgpl-3.0

tridesclous/tridesclous_examples

Brochier_motor_cortex_96_channels/Brochier_motor_cortex_96_channels.ipynb

1

110874

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Massively parallel recordings in macaque motor cortex\n", "\n", "Brochier and collaborator release public dataset recorded with 10-by-10 Utah electrode arrays on macaque motor cortex.\n", "\n", "See https://www.nature...

mit

probml/pyprobml

deprecated/simulated_annealing_2d_demo.ipynb

1

543381

{ "nbformat": 4, "nbformat_minor": 0, "metadata": { "colab": { "name": "simulated-annealing-2d.pynb", "provenance": [], "toc_visible": true, "authorship_tag": "ABX9TyP9S1O4x5pDb8rKiz1Xcsaw", "include_colab_link": true }, "kernelspec": { "name": "python3", "display_name": "Python 3" }, ...

mit

mne-tools/mne-tools.github.io

0.20/_downloads/bf3ad991f7c7776e245520709f49cb04/plot_cwt_sensor_connectivity.ipynb

1

4057

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

bsd-3-clause

balarsen/pymc_learning

Mixture/Gaussian Mixture.ipynb

1

3040946

null

bsd-3-clause

Tatiana-Krivosheev/ipython-notebooks-physics

Phys2212L.Capacitors.ipynb

1

19576

{ "metadata": { "name": "", "signature": "sha256:c8f331dfcac9cece8daec4ac0ecedd6e63b792cc34a2911afa1d88f646373747" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "heading", "level": 1, "metadata": {}, "source": [ "PHYS 2212L - Principles of...

cc0-1.0

jorgedominguezchavez/dlnd_first_neural_network

Your_first_neural_network.ipynb

1

312379

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

mit

pysal/pPysal

weights/EvenFasterSerialContiguity.ipynb

1

86431

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import time\n", "import collections\n", "import sys\n", "sys.path.append('/Users/jay/github/pysal/')\n", "imp...

bsd-3-clause

shreyasva/tensorflow

tensorflow/tools/docker/notebooks/2_getting_started.ipynb

3

143937

{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "6TuWv0Y0sY8n" }, "source": [ "# Getting Started in TensorFlow\n", "## A look at a very simple neural network in TensorFlow" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text...

apache-2.0

DawesLab/LabNotebooks

Pu Zhang QuTiP List.ipynb

1

23461

{ "cells": [ { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib as mpl\n", "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 2, "metadat...

mit

jhconning/Dev-II

notebooks/InsecureRights.ipynb

1

20065

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Causes and consequences of insecure Property Rights to land\n", "\n", "Insecure property rights to land can affect investement decisions and the nature and efficiency of agrarian production organization by affecting the marke...

bsd-3-clause

jbarratt/ipython_notebook_presentation

Command Line Demo.ipynb

1

3850

{ "metadata": { "name": "", "signature": "sha256:6cf257f44568ee9ffbdcd690881700f54d7b3728e6148a92ae40a2ad8355e55f" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# IPython Command Line examples\n", ...

cc0-1.0

jskDr/jamespy_py3

wireless/polar_nb/Sage - NPolar Transform-Copy5.ipynb

2

17235

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Symbolic Polar Transformation using Sagemath" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from sage import *\n", "import numpy as np\n", "from wirele...

mit

kkaiser/kkaiser.github.io

social_data/nb/project_assignmentB.ipynb

1

30831883

null

gpl-3.0

jfconavarrete/spts-uoe

build_full_dataset.ipynb

1

30569

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Merge Datasets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Set Up" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [],...

gpl-3.0

anodos-ru/catalog

updaters/comptek.ipynb

1

5030

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Comptek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Инициализация" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": ...

mit

GLiCom/CorpusAnalysis2016

notebooks/Practica 8.ipynb

1

3657

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Práctica 8: corpora and collocations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Ejercicio: completar el siguiente Notebook. Añadir celdas adicionales para pasos intermedios y **mostrar los resul...

apache-2.0

SiggyF/notebooks

sealevelexample.ipynb

1

497117

{ "metadata": { "name": "", "signature": "sha256:2be34651646d672442140d1fc4158c3fe65f4deb31b9a4c3a74a2136aad3fcdc" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Sea-level rise \n", "===============...

gpl-3.0