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
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alexjj/pancham | pandas-cookbook/cookbook/Chapter 4 - Find out on which weekday people bike the most with groupby and aggregate.ipynb | 1 | 140728 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
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{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"\n",
"pd.se... | unlicense |
pk-ai/training | machine-learning/deep-learning/udacity/ud730/1_notmnist.ipynb | 1 | 68918 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "5hIbr52I7Z7U"
},
"source": [
"Deep Learning\n",
"=============\n",
"\n",
"Assignment 1\n",
"------------\n",
"\n",
"The objective of this assignment is to learn about simple data curatio... | mit |
flaviostutz/datascience-snippets | kaggle-sea-lion/07-train-lion-patches-single.ipynb | 1 | 100711 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Train sea lion classifier with a convnet"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"editable":... | mit |
analyticsguru/NUPredict480FinalProject | predict480FinalProjectDataPrep.ipynb | 1 | 125571 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#supress warning message\n",
"import warnings; warnings.simplefilter(\"ignore\")"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
... | mit |
wd15/extremefill2D | notebooks/fig3a_sim.ipynb | 1 | 1599 | {
"metadata": {
"name": "fig3a_sim_nx200"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"!date\n",
"\n",
"from tools import batch_launch\n",
" \n",
"kPluses = np.logspace(1, 4, 20)... | mit |
rafaelscnunes/COS738-AutomaticPatentClassification | GitHub/classification-TF-1gram.ipynb | 1 | 303043 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Automatic Patent Classification"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import numpy as np\n",
"import pandas as pd\n",
"impo... | mit |
CompPhysics/MachineLearning | doc/Programs/JupyterFiles/Examples/Scikit-Learn Website Examples/KNNeighbors Example from Sci-Kit Learn Website.ipynb | 1 | 67660 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAA3EAAAEVCAYAAABOjmwpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxpY... | cc0-1.0 |
birdsarah/bokeh-miscellany | old/slider_example/Gapminder homage 0_3 html with population - better slice.ipynb | 1 | 2256903 | null | gpl-2.0 |
luwei0917/awsemmd_script | notebook/GlpG_paper/apr_week2_Fourth.ipynb | 1 | 4681388 | null | mit |
mattgiguere/doglodge | code/.ipynb_checkpoints/bf_qt_scraping-checkpoint.ipynb | 1 | 14699 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# bf_qt_scraping\n",
"\n",
"This notebook describes how hotel data can be scraped using PyQT.\n",
"\n",
"The items we want to extract are:\n",
"- the hotels for a given city\n",
"- links to each hotel page\n",
... | mit |
beangoben/lerningMachin | Python/Variables.ipynb | 1 | 13506 | {
"metadata": {
"name": "",
"signature": "sha256:d290d607f154fe7ba32e0579a79168cbf04ae90c48548ebabc7ebe786014e5c9"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Variables"
]
},
... | gpl-3.0 |
tayebzaidi/HonorsThesisTZ | ThesisCode/testing/LightCurve Align plots.ipynb | 1 | 39521 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import json\n",
"import numpy as np\n",
"import sys\n",
"import matplotlib.pyplot as plt\n",
"sys.path.append('../gen_lightcurves/gp_smoothed')"
]
},... | gpl-3.0 |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-E-corrected-all-ph-out-12d.ipynb | 1 | 469298 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Executed:** Mon Mar 27 11:39:24 2017\n",
"\n",
"**Duration:** 7 seconds."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# usALEX-5samples - Template\n",
"\n",
"> *This notebook is ex... | mit |
tritemio/multispot_paper | out_notebooks/usALEX-5samples-PR-raw-dir_ex_aa-fit-out-AexAem-17d.ipynb | 1 | 628102 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Executed:** Mon Mar 27 11:38:07 2017\n",
"\n",
"**Duration:** 10 seconds."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# usALEX-5samples - Template\n",
"\n",
"> *This notebook is e... | mit |
Juan-Mateos/coll_int_ai_case | notebooks/ml_topic_analysis_exploration.ipynb | 1 | 999465 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Prototype pipeline for the analysis of ML arxiv data\n",
"\n",
"We query arxiv to get papers, and then run them against Crossref event data to find social media discussion and Microsoft Academic Knowledge to find institutiona... | mit |
jmschrei/pomegranate | tutorials/old/Tutorial_7_Parallelization.ipynb | 1 | 83938 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# pomegranate and parallelization\n",
"\n",
"pomegranate supports parallelization through a set of built in functions based off of joblib. All computationally intensive functions in pomegranate are implemented in cython with th... | mit |
arsenovic/galgebra | examples/ipython/inner_product.ipynb | 1 | 21533 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"from __future__ import print_function\n",
"from sympy import Symbol, symbols, sin, cos, Rational, expand, simplify, collect, S\n",
"from galgebra.printer import Eprint, Get_Program, Print_... | bsd-3-clause |
jbliss1234/ML | t81_558_class4_class_reg.ipynb | 1 | 126785 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# T81-558: Applications of Deep Neural Networks\n",
"**Class 4: Classification and Regression**\n",
"* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), School of Engineering and Applied Science, [Washington Unive... | apache-2.0 |
MilweeScience/Turner | index_turner.ipynb | 1 | 4245 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 7th Grade Comprehensive Science Jupyter Notebooks\n",
"\n",
"\n",
"## <span style=\"color:green\">beta.mybinder.org/repo/MilweeScience/Turner</span>\n",
"\n",
"![](https://www.fullstackpython.com/img/logos/jupyter... | mit |
blagasz/python-ann | example.ipynb | 1 | 2057 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import neurolab as nl\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
... | gpl-2.0 |
michhar/csvconf2016 | csvconfv2 MHarris-python-kernel.ipynb | 1 | 1283756 | null | mit |
jobar8/interpies | notebooks/Create_Globes_with_Basemap_and_Cartopy.ipynb | 1 | 8557458 | null | bsd-3-clause |
hadibakalim/deepLearning | 01.neural_network/00.intro_and_general/.ipynb_checkpoints/00.intro-checkpoint.ipynb | 1 | 1354 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Neural Networks\n",
"\n",
"### What is all?\n",
"\n",
"\n",
"Scikit-learn\n",
"- An extremely popular Machine Learning library for python.\n",
"\n",
"Perceptrons\n",
"- The simplest form of a neura... | mit |
SteveDiamond/cvxpy | examples/notebooks/WWW/max_entropy.ipynb | 2 | 5361 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Entropy maximization\n",
"\n",
"A derivative work by Judson Wilson, 6/2/2014.<br>\n",
"Adapted from the CVX example of the same name, by Joëlle Skaf, 4/24/2008.\n",
"\n",
"## Introduction\n",
"\n",
"Consid... | gpl-3.0 |
agmarrugo/sensors-actuators | notebooks/Ex_2_3.ipynb | 1 | 31939 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# The transfer function\n",
"\n",
"__Analytic form of transfer function__. In certain cases the transfer function is available as an analytic expression. One common transfer function used for resistance temperature sensors (to ... | mit |
dougbrose/data-science | ch17_fig7.ipynb | 1 | 32680 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true
},
"source": [
"### Ch17 Figure7"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
... | mit |
nukui-s/TripletEmbedding | workspace/FB40k.ipynb | 1 | 674560 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from knowledge import FBManager\n",
"from tripletembed.model import TripletEmbedding\n",
"import numpy as np\n",
"import pandas as pd\n",
"\n"
]
},
... | mit |
rayjustinhuang/DataAnalysisandMachineLearning | Linear Programming with OR-Tools.ipynb | 1 | 7762 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linear Programming with OR-Tools\n",
"\n",
"In this notebook, we do some basic LP solving with Google's OR-Tools. Problems used will be examples in Hamdy Taha's Operations Research: An Introduction, 9th Edition, which I have ... | mit |
RaRe-Technologies/gensim | docs/notebooks/nmslibtutorial.ipynb | 5 | 301875 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Similarity Queries using Nmslib Tutorial"
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial is about using the ([Non-Metric Space Library (NMSLIB)](https://github.com... | lgpl-2.1 |
dipanjanS/BerkeleyX-CS100.1x-Big-Data-with-Apache-Spark | Week 2 - Introduction to Apache Spark/lab1_word_count_student.ipynb | 1 | 33229 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# + \n",
"# **Word Count Lab: Building a ... | mit |
guillaume-chevalier/LSTM-Human-Activity-Recognition | LSTM.ipynb | 1 | 213498 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <a title=\"Activity Recognition\" href=\"https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition\" > LSTMs for Human Activity Recognition</a>\n",
"\n",
"Human Activity Recognition (HAR) using smartphones datase... | mit |
openai/openai-python | examples/embeddings/Get_embeddings.ipynb | 1 | 2512 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Get embeddings\n",
"\n",
"The function `get_embedding` will give us an embedding for an input text."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
... | mit |
telegraphic/allantools | examples/gradev-demo.ipynb | 1 | 45517 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## GRADEV: gap robust allan deviation\n",
"\n",
"Notebook setup & package imports"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
... | gpl-3.0 |
badlands-model/BayesLands | Examples/etopo/etopoGen.ipynb | 1 | 5162950 | null | gpl-3.0 |
tonyfast/tidy-harness | harness/ext/base.ipynb | 1 | 4259 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"try:\n",
" from ..base import AttributeObject\n",
"except:\n",
" from harness.python.base import AttributeObject\n",
"\n",
"from toolz.curried ... | bsd-3-clause |
ethen8181/machine-learning | model_selection/partial_dependence/partial_dependence.ipynb | 1 | 342015 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": "true"
},
"source": [
"<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
"<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Partial-Dependence-Plot\" data-toc-modified-id=\"Partial-Dependence-Plot-1\"><span ... | mit |
Elucidation/ChessboardDetect | Chessboard Detect.ipynb | 1 | 1672098 | null | mit |
eshlykov/mipt-day-after-day | statistics/hw-13/hw-13.3.ipynb | 1 | 38520 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Теоретическое домашнее задание 13\n",
"### Задача 3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Используя метод линейной регрессии, постройте приближение функции $f$ многочленом третьей сте... | unlicense |
zaqwes8811/micro-apps | self_driving/deps/Kalman_and_Bayesian_Filters_in_Python_master/Supporting_Notebooks/Converting-Multivariate-Equations-to-Univariate.ipynb | 1 | 8125 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <style>\n",
" .output_wrapper, .output {\n",
" height:auto !important;\n",
" max-height:100000px;... | mit |
idemello/idemello.github.io | proj2.ipynb | 1 | 1654031 | null | mit |
wcmckee/wcmckee-notebook | sortbooks.ipynb | 1 | 11660 | {
"metadata": {
"name": "",
"signature": "sha256:475345588e89763d8e94a7840429503651a555e2e7fd35c8f58cd71be69df87a"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a script to get all notebooks from... | gpl-2.0 |
zipeiyang/liupengyuan.github.io | chapter2/homework/computer/5-17/201611680890-5.17.ipynb | 13 | 4450 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import random\n",
"\n",
"def win():\n",
" print(\n",
" '''\n",
" ======恭喜你，你赢了=======\n",
" \n",
" \n",
" ... | mit |
ecabreragranado/OpticaFisicaII | OCT/OCT.ipynb | 1 | 12016 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Tomografía de coherencia óptica. Puntos básicos."
]
},
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"*La tomografía de coherencia óptica (TCO) es una... | gpl-3.0 |
dsevilla/bdge | pig-hive/hive.ipynb | 1 | 1606 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# NoSQL (Hive)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!pip install apache-airf... | mit |
MMaus/mutils | mmnotebooks/old/AnkleSlip - temporary NB.ipynb | 1 | 86853 | {
"metadata": {
"name": "AnkleSlip - temporary NB"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## load data from temporary file"
]
},
{
"cell_type": "code",
"collapsed": false,
... | gpl-2.0 |
pycrystem/pycrystem | doc/demos/02 GaAs Nanowire - Phase Mapping - Orientation Mapping.ipynb | 1 | 198354 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Phase/Orientation Mapping"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This tutorial demonstrates how to achieve phase and orientation mapping via scanning electron diffraction using both pattern ... | gpl-3.0 |
zpenoyre/illustris | apiTestNotebook.ipynb | 1 | 47392 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The autoreload extension is already loaded. To reload it, use:\n",
" %reload_ext autoreload\n"
]
}
],
"source": [
... | mit |
evanbiederstedt/RRBSfun | QC_filtered50K/regression_methylation_weighted_10August2016_avgReadCpgs_gtreql3.8CpG_filter50K.ipynb | 1 | 4918858 | null | mit |
Tahsin-Mayeesha/Udacity-Machine-Learning-Nanodegree | projects/boston_housing/boston_housing.ipynb | 1 | 201477 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Machine Learning Engineer Nanodegree\n",
"## Model Evaluation & Validation\n",
"## Project 1: Predicting Boston Housing Prices\n",
"\n",
"Welcome to the first project of the Machine Learning Engineer Nanodegree! In th... | mit |
csdms/pymt | notebooks/sedflux3d_and_child.ipynb | 1 | 444006 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Sedflux3D + CHILD\n",
"* Link to this notebook: https://github.com/csdms/pymt/blob/master/notebooks/sedflux3d_and_child.ipynb\n",
"* Install command: `$ conda install notebook pymt_sedflux pymt_child`\n",
"\n"
]
},
... | mit |
davofis/computational_seismology | 05_pseudospectral/cheby_derivative_solution.ipynb | 1 | 8155 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div style='background-image: url(\"../../share/images/header.svg\") ; padding: 0px ; background-size: cover ; border-radius: 5px ; height: 250px'>\n",
" <div style=\"float: right ; margin: 50px ; padding: 20px ; background: rgb... | gpl-3.0 |
CoderDojoTC/python-minecraft | classroom-code/examples/hello_world.ipynb | 1 | 480 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"#!/usr/bin/python\n",
"import mcpi.minecraft as minecraft\n",
"import mcpi.block as block\n",
"\n",
"# Connect to the Minecraft server\n",
"world ... | mit |
ES-DOC/esdoc-jupyterhub | notebooks/cccr-iitm/cmip6/models/sandbox-1/ocean.ipynb | 1 | 164419 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Ocean \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: CCCR-IITM \n",
"**Source ID**: SANDBOX-1 \n",
"**... | gpl-3.0 |
RobbieNesmith/PandasTutorial | Tutorial/Exercises-4.ipynb | 2 | 218081 | {
"cells": [
{
"cell_type": "code",
"execution_count": 48,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"import seaborn as sbn\n",
"sbn.set()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"me... | mit |
FerdinandKlingenberg/TestAvSentinel-2Python | Resample.ipynb | 1 | 8516 | {
"cells": [
{
"cell_type": "code",
"execution_count": 70,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Import modules\n",
"import ndvi_algo\n",
"import snappy\n",
"from snappy import ProductIO\n",
"from snappy import GPF\n",
"from snappy import jpy\n... | mit |
Ledoux/ShareYourSystem | Ouvaton/Hdformater.ipynb | 1 | 7247 | {
"nbformat": 3,
"worksheets": [
{
"cells": [
{
"source": "\n<!--\nFrozenIsBool False\n-->\n\n#Hdformater\n\n##Doc\n----\n\n\n> \n> An Hdformater instance maps an apply and so \"grinds\" a MappingArgDictsList \n> to a method.\n> \n> \n\n----\n\n<small>\nView the Hdformater notebook on [... | mit |
dsg-bielefeld/deep_disfluency | demos/demo.ipynb | 1 | 11674 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"no installed deep_disfluency package, pathing to source\n"
]
}
],
"source": [
"try:\n",
... | mit |
cbpygit/pypmj | examples/Post process management.ipynb | 1 | 5014 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Preparations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports and configuration"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
... | gpl-3.0 |
camilogavo/Colombian_Energy_Forecasting | Model_over_all_serie.ipynb | 1 | 344983 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"#Carga de paquetes a utilizar en este documento\n",
"library(forecast)\n",
"library(dplyr)\n",
"library(ggplot2)\n",
"library(readr)\n",
"library(tidyr)\n",
"library(lubrid... | mit |
steve-federowicz/om | examples/.ipynb_checkpoints/Untitled5-checkpoint.ipynb | 2 | 181 | {
"metadata": {
"name": "",
"signature": "sha256:421f621054b55c4937bb9cdce02c50b7d5c4525468b667f50ee49eef8ebcc38d"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": []
} | mit |
joaquimargente/qisskit-ipynb | QuantumClient.ipynb | 1 | 4110 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# USER, PLEASE SET CONFIG:\n",
"token = \"_TOKEN_\"\n",
"config = {\n",
" \"url\": 'https://quantumexperience.ng.bluemix.net/api'\n",
"}\n",
"# -... | apache-2.0 |
dataminingapp/dataminingapp-lectures | Lecture-4/Distance-Functions.ipynb | 2 | 15481 | {
"metadata": {
"celltoolbar": "Raw Cell Format",
"name": "",
"signature": "sha256:d9c1b07d11a11d66b255616a18846d56b29fb1c35f5f1a34f3fd14a8e7d99bcc"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source":... | mit |
santiago-salas-v/lit-imp | CO2 Equilibria.ipynb | 1 | 34745 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CO2 Equilibria"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1 - Atm. pressure (func. of altitude)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$dp = -\\rho ... | mit |
daviddesancho/Cossio | notebooks/cossio_brownian_cython_calibrate.ipynb | 1 | 1162260 | null | mit |
wholden/ArduiArcStepper | .ipynb_checkpoints/TestingArduinoMotorErrors-checkpoint.ipynb | 1 | 27636 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from arduinostepper import arduinostepper as ardstep"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"collapsed": true
},
"out... | mit |
megatharun/basic-python-for-researcher | Tutorial 3 - Conditional Expression.ipynb | 2 | 17382 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# <span style=\"color: #B40486\">BASIC PYTHON FOR RESEARCHERS</span>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_by_ [**_Megat Harun Al Rashid bin Megat Ahmad_**](https://www.researchgate.net/prof... | artistic-2.0 |
barjacks/pythonrecherche | 11 pandas plotting, re, selenum practice/02 Reading local files.ipynb | 1 | 31491 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import os\n",
"from bs4 import BeautifulSoup\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Read fil... | mit |
stitchfix/d3-jupyter-tutorial | 3d_meshing.ipynb | 1 | 57578 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 3D Visualization of a Convex Hull with D3"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook provides a simple example of convex hull visualization using D3."
]
},
{
"cell_type":... | mit |
junpenglao/Bayesian-Cognitive-Modeling-in-Pymc3 | CaseStudies/NumberConceptDevelopment.ipynb | 1 | 2485569 | null | gpl-3.0 |
zhongyuanzhou/FCH808.github.io | Theano/Theano - Deep NNet dropout & rectifiers.ipynb | 4 | 8169 | {
"metadata": {
"name": "",
"signature": "sha256:9990c95b3c79dff37ddc76d4ef9f7b88390aa7307f4f175145c2c079585fc530"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import theano\n",
"from theano import... | mit |
mvdbosch/AtosCodexDemo | Jupyter Notebooks/Explore the CBS Crime and Demographics Dataset.ipynb | 1 | 170171 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Atos Codex - Data Scientist Workbench\n",
"### Explore the CBS Crime and Demographics Dataset\n",
"First check some of the environment specs and see what we have here"
]
},
{
"cell_type": "code",
"execution_count... | gpl-3.0 |
ramseylab/networkscompbio | class08_components_python3.ipynb | 1 | 5066 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CS446/546 - Class Session 8 - Components\n",
"\n",
"In this class session we are going to find the number of proteins that are in the giant component of the (undirected) protein-protein interaction network, using igraph."
... | apache-2.0 |
ES-DOC/esdoc-jupyterhub | notebooks/miroc/cmip6/models/sandbox-3/land.ipynb | 1 | 173500 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Land \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: MIROC \n",
"**Source ID**: SANDBOX-3 \n",
"**Topic... | gpl-3.0 |
hzuosit/ctci_answer_python | Chapter_4/graph_class.ipynb | 2 | 775 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#Graph class\n",
"from graph_node_class import node\n",
"class graph:\n",
" def __init__(self,root = None):\n",
" self.root = root\n",
" ... | mit |
ES-DOC/esdoc-jupyterhub | notebooks/nuist/cmip6/models/sandbox-2/landice.ipynb | 1 | 37248 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Landice \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: NUIST \n",
"**Source ID**: SANDBOX-2 \n",
"**To... | gpl-3.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/official/ml_metadata/sdk-metric-parameter-tracking-for-custom-jobs.ipynb | 1 | 33266 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "ur8xi4C7S06n"
},
"outputs": [],
"source": [
"# Copyright 2022 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
... | apache-2.0 |
dnxbjyj/python-basic | libs/ConfigParser/handout.ipynb | 1 | 7504 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 用ConfigParser模块读写conf配置文件"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ConfigParser是Python内置的一个读取配置文件的模块，用它来读取和修改配置文件非常方便，本文介绍一下它的基本用法。"
]
},
{
"cell_type": "markdown",
"metadata": {}... | mit |
damienstanton/tensorflownotes | 6_lstm.ipynb | 1 | 42631 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "8tQJd2YSCfWR"
},
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "D7tqLMoKF6uq"
},
"source": [
"Deep Learning\n",
"=============\n",
"\n",
... | mit |
google/floq-client | samples/notebooks/Floq_Client_Colab_Tutorial.ipynb | 1 | 16285 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Floq Client Colab Tutorial",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"language_info": {
"name": "python"
}
},... | apache-2.0 |
m2dsupsdlclass/lectures-labs | labs/09_triplet_loss/triplet_loss_totally_looks_like.ipynb | 1 | 21667 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Triplet Loss on Totally Looks Like dataset\n",
"\n",
"This notebook is inspired from [this Keras tutorial](https://keras.io/examples/vision/siamese_network/) by Hazem Essam and Santiago L. Valdarrama.\n",
"\n",
"The ... | mit |
mne-tools/mne-tools.github.io | 0.22/_downloads/243172b1ef6a2d804d3245b8c0a927ef/plot_60_maxwell_filtering_sss.ipynb | 2 | 19280 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Signal-spa... | bsd-3-clause |
dragly/doconce | doc/pub/ipynb/info.ipynb | 3 | 20895 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- dom:TITLE: Special DocOnce features for Jupyter notebooks -->\n",
"# Special DocOnce features for Jupyter notebooks\n",
"<!-- dom:AUTHOR: Hans Petter Langtangen at Simula & University of Oslo -->\n",
"<!-- Author: --> ... | bsd-3-clause |
atulsingh0/MachineLearning | BMLSwPython/01_GettingStarted_withPython.ipynb | 1 | 224623 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# import\n",
"import numpy as np\n",
"import scipy as sp\n",
"import timeit\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline"
... | gpl-3.0 |
NicholasBermuda/transit-fitting | notebooks/demo.ipynb | 2 | 1507454 | null | mit |
Prooffreader/intro_machine_learning | 08_Dimensionality_Reduction.ipynb | 1 | 53609 | {
"metadata": {
"name": "",
"signature": "sha256:55da80124d4fb776789c8deec8f1a94af945058ee2ed7d6eb47aed6679e8e09f"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction to data analysis using mach... | mit |
tynbl/tynbl.github.io | docs/python-xxxy-2/00/01/coding_utf8_example.ipynb | 2 | 1065 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# coding:utf-8\n",
"\n",
"'''\n",
"Created on Dec 3, 2016\n",
"\n",
"@author: Bin Liang\n",
"'''\n",
"\n",
"\n",
"def run_main():\n... | mit |
justanr/notebooks | fun.itertools.ipynb | 1 | 9650 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Originally, I saw this issue in a post on /r/learnpython:\n",
"\n",
" '''\n",
" if (n%a==0) or (n%b==0) or (... | mit |
mjbrodzik/ExploringCETB | Reading CETB files.ipynb | 1 | 4812100 | null | apache-2.0 |
chongxi/spiketag | spiketag/analysis/tests/Decoding_dusty.ipynb | 1 | 2994418 | null | bsd-3-clause |
uwkejia/Clean-Energy-Outlook | examples/Demo.ipynb | 2 | 7734 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Examples"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Importing libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outp... | mit |
moizumi99/CVBookExercise | Chapter-1/CV Book Ch1 Ex 6.ipynb | 1 | 23325 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from numpy import *\n",
"from numpy import random\n",
"from scipy.ndimage import filters\n",
"from PIL import *\n",
"from pylab import *\n",
"from sci... | unlicense |
mne-tools/mne-tools.github.io | 0.23/_downloads/c7633c38a703b9d0a626a5a4fa161026/psf_ctf_label_leakage.ipynb | 2 | 10377 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Visualize so... | bsd-3-clause |
xpmethod/middlemarch-critical-histories | old/e1/e1c-dehyphenate.ipynb | 2 | 31442 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Experiment 1-C dehyphenate\n",
"\n",
"Improvements to the text matcher. Stemming, etc. Strip sequences of hyphen plus space."
]
},
{
"cell_type": "code",
"execution_count": 66,
"metadata": {
"collapsed": f... | gpl-3.0 |
revspete/self-driving-car-nd | sem1/p2-traffic-sign-classifier/Traffic_Sign_Classifier.ipynb | 1 | 2033519 | null | mit |
allentran/reinforcement-learning | MC/Off-Policy MC Control with Weighted Importance Sampling.ipynb | 1 | 461646 | {
"cells": [
{
"cell_type": "code",
"execution_count": 25,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import gym\n",
"import matplotlib\n",
"import numpy as np\n",
"import sys\n",
"\n",
"from collections import de... | mit |
c4fcm/oped-gender-report | data_acquisition/Opinion Byline Statistics, New York Times LA Times Washington Post.ipynb | 1 | 489579 | {
"metadata": {
"name": "",
"signature": "sha256:940f75de9540a72ccb338bf9a98bf355489253e70514f1688c5afb5dcc350949"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"#TODO: words written by men and women\n",
... | mit |
kriukov/interval-methods | ipynb/.ipynb_checkpoints/pi from area-checkpoint.ipynb | 1 | 71642 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"$y = x^2$"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"using PyPlot"
]
},
{
"cell_type": "code",
"execution_count": 2,
... | gpl-3.0 |
tensorflow/docs | site/en/guide/dtensor_overview.ipynb | 2 | 43204 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "1ljvLya59ep5"
},
"source": [
"##### Copyright 2019 The TensorFlow Authors.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"cellView": "form",
... | apache-2.0 |
Xilinx/BNN-PYNQ | notebooks/CNV-QNN_Cifar10.ipynb | 1 | 2225893 | null | bsd-3-clause |
jbpoline/cnv_analysis | CNV_dangerosite.ipynb | 1 | 36579 | {
"cells": [
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
},
{
"name": "stderr",
... | artistic-2.0 |
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