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
sauloal/ipython	probes/probes_SL2.40sc04878.ipynb	1	587888	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parameters" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Column Names\n...	mit
dsacademybr/PythonFundamentos	Cap05/Notebooks/DSA-Python-Cap05-02-Objetos.ipynb	1	8486	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>\n", "\n", "## Download: http://github.com/dsacademybr" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "out...	gpl-3.0
dinkelk/PyDyGraphs	examples/PyDyGraphTester.ipynb	1	388517	{ "cells": [ { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import pydygraphs and numpy\n", "import sys\n", "sys.path.append(\"../\")\n", "\n", "import dygraphs.graph as dy\n", "import numpy as np" ] },...	mit
tschinz/iPython_Workspace	01_Mine/MachineLearning/tensorflow-examples_nb/2_BasicModels/gradient_boosted_decision_tree.ipynb	3	32353	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient Boosted Decision Tree\n", "\n", "Implement a Gradient Boosted Decision tree (GBDT) with TensorFlow to classify\n", "handwritten digit images. This example is using the MNIST database of\n", "handwritten digit...	gpl-2.0
marcinofulus/ProgramowanieRownolegle	CUDA/iCSE_PR_map2d.ipynb	2	137134	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Zastosowanie indeksowania wielowymiarowego\n", "\n", "\n", "Zadanie: Oblicz wartości funkcji $\\sin(x^2+y^2)$ na siatce w zadanym obszarze.\n", "\n", "### Krok pierwszy\n", "\n", "Napiszemy jądro obliczaj...	gpl-3.0
ual/hedonic-models	sales-hedonic.ipynb	1	28218	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial on Hedonic Regression\n", "\n", "This material uses Python to demonstrate some aspects of hedonic regression, but the objective here is not to learn to program, but to understand the hedonic regression methodology. ...	bsd-3-clause
btw2111/intro-numerical-methods	0_intro_numerical_methods.ipynb	8	82547	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introduction and Motivation: Modeling and methods for scientific computing" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "sli...	mit
computational-class/computational-communication-2016	qinqiang/homework3/homework_3.ipynb	1	13080	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "吸大麻真的不会上瘾吗？High一下的代价比你想象得大\n", "2016-04-20\n", "\n", "微信ID:ibookreview『每个早晨，与534000人一起阅读』据说，...	mit
matthewzimmer/traffic-sign-classification	Traffic_Signs_Recognition_Cifar10.ipynb	1	2515092	null	mit
hande-qmc/hande	tools/pyhande/tutorials/3_custom_get_results_ccmc.ipynb	1	8243	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This demonstration shows how CCMC [1] data (analysis) results can be analysed in a more customised way. \n", "This applies to FCIQMC [2] as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": {},...	lgpl-2.1
PYPIT/PYPIT	doc/comparisons/PYPIT_vs_LowRedux_Kastb.ipynb	1	1350742	null	gpl-3.0
nmiculinic/psiml2017-facs	TestSpredSheet.ipynb	1	2720	{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from openpyxl import load_workbook\n", "import os" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "ou...	mit
ComputationalModeling/spring-2017-danielak	past-semesters/fall_2016/day-by-day/day15-Schelling-1-dimensional-segregation-day2/Day_15_Pre_Class_Notebook.ipynb	1	5010	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting ready to implement the Schelling model\n", "\n", "## Goal for this assignment\n", "\n", "The goal of this assignment is to finish up the two functions that you started in class on the first day of this project...	agpl-3.0
andypetrella/spark-notebook	notebooks/core/Spark-101.snb.ipynb	5	13224	{ "metadata" : { "name" : "Spark 101", "user_save_timestamp" : "1970-01-01T01:00:00.000Z", "auto_save_timestamp" : "2015-01-10T00:02:12.659Z", "language_info" : { "name" : "scala", "file_extension" : "scala", "codemirror_mode" : "text/x-scala" }, "trusted" : true, "custom...	apache-2.0
minesh1291/Practicing-Kaggle	MNIST_2017/dump_/talking_keras_notebook.ipynb	1	16708	{ "cells": [ { "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "# This Python 3 environment comes with many helpful...	gpl-3.0
UWSEDS/LectureNotes	Fall2018/09_UnitTests/unit-tests.ipynb	1	17995	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Unit Tests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...	bsd-2-clause
xmnlab/notebooks	jupyter/Introducción.ipynb	1	66814	{ "cells": [ { "cell_type": "markdown", "metadata": { "toc": "true" }, "source": [ "# Table of Contents\n", " <p><div class=\"lev1 toc-item\"><a href=\"#Introducción-a-Jupyter-Notebook\" data-toc-modified-id=\"Introducción-a-Jupyter-Notebook-1\"><span class=\"toc-item-num\">1  </spa...	mit
kgourgou/set-phasers-to-stan	birth-death-paths-stan/.ipynb_checkpoints/paths-checkpoint.ipynb	1	2375	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib.pyplot import plot\n", "from ssa import SSA\n", "from pystan import stan\n", "\n", "%matplotlib inline" ] }, { "cell_type": "co...	mit
yl565/statsmodels	examples/notebooks/ols.ipynb	3	12634	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ordinary Least Squares" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from __future__ impor...	bsd-3-clause
kiaderouiche/hilbmetrics	notebooks/convexGeometry.ipynb	2	1108	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## This IPython notebook contains the example code presented symbolic calculs in Convex Hull " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inlin...	apache-2.0
margulies/gradient_analysis	06_supplementary_analysis_and_figures.ipynb	2	82762	{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Components variance" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", ...	mit
queirozfcom/python-sandbox	python3/notebooks/pandas-columns/main.ipynb	1	65323	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## accompanying blog post: http://queirozf.com/entries/pandas-dataframe-examples-column-operations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plai...	mit
ssunkara1/bqplot	examples/Advanced Plotting/Animations.ipynb	1	36869	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Animations of marks and axes can be enabled by setting 'animation_duration' property of the figure" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, ...	apache-2.0
marioberges/F16-12-752	lectures/Lecture2.ipynb	1	14519	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture \\#2: Setting up your Development Environment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is what I intend to cover today:\n", "\n", "* Python Basics\n", "* What is Inter...	gpl-3.0
GoogleCloudPlatform/cloudml-samples	notebooks/xgboost/TrainingWithXGBoostInCMLE.ipynb	1	18649	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XGBoost Training on AI Platform\n", "This notebook uses the [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) to demonstrate how to train a model on Ai Platform." ] }, { "cell_type": "markd...	apache-2.0
aksp/vrview	examples/orientations/optical-flow-to-important-points.ipynb	1	41091	{ "cells": [ { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "videos/probation-reg-small.mp4\n" ] }, { "name": "stderr", "output_type": "stream", ...	apache-2.0
mdiaz236/DeepLearningFoundations	Untitled.ipynb	1	2115	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[3.0, 3.5, 4.199999999999999]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ...	mit
bollwyvl/nosebook	tests/py3/ipy3/Scrubbing.ipynb	1	3737	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "from IPython import display" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outpu...	bsd-3-clause
wrossmorrow/sgsb	baseball.ipynb	1	1580107	null	apache-2.0
google/jax-cfd	notebooks/ml_model_inference_demo.ipynb	1	951934	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "dq5Hou4QzH0Q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2dec9716-08bc-457f-c937-1a00e12e13da" }, "outputs": [ { "output_t...	apache-2.0
GoogleCloudPlatform/training-data-analyst	courses/machine_learning/deepdive/01_bigquery/labs/c_extract_and_benchmark.ipynb	2	15482	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Extract Datasets and Establish Benchmark\n", "\n", "Learning Objectives\n", "- Divide into Train, Evaluation and Test datasets\n", "- Understand why we need each\n", "- Pull data out of BigQuery and into CSV\n...	apache-2.0
MassimoSchiappa/datascience	FinaleExamUCSD+DSE200x.ipynb	1	8779948	null	apache-2.0
lemonyhermit/CodingYoga	python-for-developers/Chapter1/Chapter1_Introduction.ipynb	1	15763	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Python for Developers](http://ricardoduarte.github.io/python-for-developers/#content)\n", "==========================\n", "First edition\n", "-----------------------------------\n", "\n", "Chapter 1\n", "======...	gpl-2.0
zhouqifanbdh/liupengyuan.github.io	chapter1/homework/computer/3-22/201611680345 .ipynb	54	5546	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入你的姓名，以回车结束。lai\n", "请输入你的出生日期，以回车结束。6.04\n", "lai 你是双子座\n" ] } ], "source": [ ...	mit
mne-tools/mne-tools.github.io	0.14/_downloads/plot_sensors_decoding.ipynb	1	5391	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n# Decoding sensor sp...	bsd-3-clause
getmykhan/PythonforDataAnalytics	Machine Learning/Pokemon.ipynb	1	631950	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\WorkArea\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0...	gpl-3.0
zambzamb/zpic	python/Animation.ipynb	1	5158	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Animation using direct access to simulation data\n", "\n", "In this more advanced example, we show how to create a movie of the particles phasespace using direct access to the simulation data. The initialization of the simula...	agpl-3.0
yigong/AY250	hw4/hw4_training.ipynb	1	9498	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# import\n", "#!/usr/bin/env python\n", "import matplotlib.pyplot as plt\n", "from glob import glob\n", "impo...	mit
bamford/gzhubble	python/creating_debiased_catalog/STEP_1_p_features_thresholds_slope_method.ipynb	1	835447	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 1: Analyzing FERENGI Data \n", "## Determine correctable / uncorrectable regions of z/mu space. Ferengi galaxies that are considered 'correctable' are used to compute zeta in Step 2. This also generates low and hi limit valu...	mit
karlstroetmann/Artificial-Intelligence	Python/6 Classification/Polynomial-Logistic-Regression.ipynb	1	22080	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "with open (\"../style.css\", \"r\") as file:\n", " css = file.read()\n", "HTML(css)" ] }, { "cell_ty...	gpl-2.0
tpin3694/tpin3694.github.io	machine-learning/.ipynb_checkpoints/loading_scikit-learns_digits-dataset-checkpoint.ipynb	1	8545	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Loading Scikit-Learn's Digits Dataset \n", "Slug: loading_scikit-learns_digits-dataset \n", "Summary: Loading the built-in digits datasets of Scikit-Learn. \n", "Date: 2016-08-31 12:00 \n", "Category: Machin...	mit
CompPhysics/MachineLearning	doc/Programs/JupyterFiles/Examples/Housing.ipynb	1	631949	{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxp...	cc0-1.0
Joshuaalbert/IonoTomo	src/ionotomo/notebooks/PropagationPathlengthVar.ipynb	1	44135	{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ImportError", "evalue": "No module named 'Logger'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------...	apache-2.0
georgetown-analytics/yelp-classification	Yelp_web_scrapper/Review_Scrapper.ipynb	1	38663	{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import requests\n", "import re\n", "import json\n", "import sys\n", "sys.path.append('/Users/robertsonwang/Desktop...	mit
tensorflow/docs-l10n	site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb	1	23515	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "DjUA6S30k52h" }, "source": [ "##### Copyright 2021 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...	apache-2.0
c22n/ion-channel-ABC	docs/examples/crn/crn_ina_testing.ipynb	1	204814	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Create and test ion channel model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:myokit:Loadin...	gpl-3.0
mdastro/Challenge	Coding/SanityCheckAll.ipynb	1	170767	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/mldantas/miniconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib ...	mit
davidvilla/python-course	iteradores.ipynb	1	9956	{ "metadata": { "name": "", "signature": "sha256:aecedc9996af45fc57408c5a0e8e47ebf9a40244087ac323522ec0b9556afa64" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Creando un iterador" ] }, ...	gpl-2.0
mne-tools/mne-tools.github.io	0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb	1	10262	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\nSource localiz...	bsd-3-clause
PyPSA/PyPSA	examples/notebooks/scigrid-sclopf.ipynb	2	3565	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Security-Constrained Optimisation\n", "\n", "In this example, the dispatch of generators is optimised using the security-constrained linear OPF, to guaranteed that no branches are overloaded by certain branch outages." ] ...	mit
mementum/backtrader	samples/pyfoliotest/backtrader-pyfolio.ipynb	1	1470926	null	gpl-3.0
oresat/oresat-design	SatReports.ipynb	1	34317	{ "metadata": { "name": "", "signature": "sha256:e09322625ea94dd3a92b5256503244c5520f56dc10befd459de92aa0247a46b4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import ephem\n", "import numpy as np\...	gpl-3.0
mne-tools/mne-tools.github.io	0.12/_downloads/plot_interpolate_bad_channels.ipynb	1	2308	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "execution_count": null, "cell_type": "code", "source": [ "%matplotlib inline" ], "outputs": [], "metadata": { "collapsed": false } }, { "source": [ "\n====================...	bsd-3-clause
dominikgrimm/ridge_and_svm	Toy-Example-Solution.ipynb	1	225315	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Toy Example: Ridge Regression vs. SVM\n", "<p></p>\n", " \n", "<div style=\"text-align:justify\">\n", " In this toy example we will compare two machine learning models: <em>Ridge Regression</em> and <em>C-SVM</e...	mit
erfannoury/TextClassification	sklearn-text classification.ipynb	2	112878	{ "metadata": { "name": "", "signature": "sha256:b120c5bed47d95f008362ba2a2a459b1463e2594f34b1a08044be28d28fa31df" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import scipy ...	mit
ziky5/F4500_Python_pro_fyziky	lekce_05/B.ipynb	1	2557	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def B(t, r):\n", " \"\"\"Returns magnetic field of a current loop in point r = (r1, r2, r3) and time t\n", " Th...	mit
jasag/Phytoliths-recognition-system	code/notebooks/Prototypes/Image_Labeler_Prototypes/Image_Labeler_prototype2.ipynb	1	17921	{ "cells": [ { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "import ipywidgets as widgets\n", "from traitlets import Unicode, validate, Bool, Any\n", "from ipywidgets import C...	bsd-3-clause
balarsen/pymc_learning	combine_counts/Combine counts and resample.ipynb	1	521123	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Idea for spectrogram error bars\n", "\n", "1. For each pixel get the dist that go into thwt ais plotted. \n", "1. Resample each based on the dist\n", "1. replot\n", "1. repeat and see which are still there" ] }...	bsd-3-clause
kimlaborg/NGSKit	notebooks/1-PWM_basics.ipynb	1	54824	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PWMs\n", "\n", "This notebook is a short example of how to generate a PWM from a MSA, and a few analyses you can do using Ngskit. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "Execute...	mit
catalystcomputing/DSIoT-Python-sessions	Session3/code/03 Supervised Learning - 00 Python basics and Logistic Regression.ipynb	1	14363	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Here we introduce Data science by starting with a common regression model(logistic regression). The example uses the Iris Dataset\n", "# We also introduce Pytho...	apache-2.0
helgako/cms-dqm	notebooks/CMS-AE.ipynb	1	1070552	null	mit
kmunve/APS	aps/notebooks/ml_varsom/linear_regression.ipynb	1	35332	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LINEAR REGRESSION\n", "\n", "- is the simplest machine learning model\n", "- is used for finding linear relationship between target and one or more predictors\n", "- there are two types of linear regression:\n", "...	mit
alexjj/money-scripts	UK Index Prices.ipynb	1	5161	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'selenium'", "output_type": "error", "traceback": [ "\u001b[0;31m--------------------------------------------------------------...	bsd-2-clause
ComputationalModeling/spring-2017-danielak	past-semesters/spring_2016/day-by-day/day18-kinematics-terminal-velocity-of-a-skydiver/video_stuff/example_function.ipynb	2	1544	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook calculates an example function that we'll be using in our videos of numerical integration and differentiation!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false ...	agpl-3.0
hdesmond/StatisticalMethods	lessons/2.UnderstandingFromData.ipynb	1	1543	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PHYS366: Statistical Methods in Astrophysics\n", "\n", "\n", "# Lesson 2: Understanding From Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Goals for this session:\n", "\n",...	gpl-2.0
leewujung/trex_fish	Split-window normalizer and total energy variation.ipynb	1	528171	{ "cells": [ { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if isunix\n", " addpath('~/internal_2tb/Dropbox/0_CODE/MATLAB/saveSameSize');\n", " addpath(['~/internal_2tb/Dropbox/0_CODE/trex_fish/Triplet_processing_...	gpl-3.0
scottgigante/m-phate	examples/classification_keras.ipynb	1	16352780	null	gpl-3.0
DJCordhose/ai	notebooks/unsupervised/pca-intuition.ipynb	1	42188	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inli...	mit
ncfausti/machine_learning	Week 2.ipynb	1	2202	{ "metadata": { "name": "", "signature": "sha256:3af4550ebf00deb5c2ca529d7bc510bbd041151233f037119e7831e9ee591e6a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Week 2\n", "\n", "####Matrix ...	gpl-3.0
djangogo/CoCo-Matlab-API-on-Windows	PythonAPI/pycocoEvalDemo.ipynb	2	517049	{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from pycocotools.coco import COCO\n", "from pycocotools.cocoeval import COCOeval\n", "import...	bsd-2-clause
eds-uga/cbio4835-sp17	lectures/Lecture23.ipynb	1	2013925	null	mit
aswolf/xmeos	examples/test-RTpress-MgSiO3/06-compare-miegruneisen-EOS.ipynb	1	12414	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib notebook\n", "import numpy as np\n", "import pandas as pd\n", "import pickle\n", "\n", "import xmeos\n", "from xmeos...	mit
sansbacon/nba	examples/machine_learning.ipynb	1	49100	{ "metadata": { "name": "", "signature": "sha256:28944676c90a8f3613d515070149629e59524ed4f81fa592b84f48df6a911150" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext sql\n", "%matplotlib inline...	mit
nikken1/patentprocessor	notebooks/MySQL.ipynb	6	841125	{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import create_engine, MetaData, Table, inspect\n", "from sqlalchemy.sql import select\n", "from sqlalchem...	bsd-2-clause
ES-DOC/esdoc-jupyterhub	notebooks/cas/cmip6/models/sandbox-3/ocean.ipynb	1	164407	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Ocean \n", "MIP Era: CMIP6 \n", "Institute: CAS \n", "Source ID: SANDBOX-3 \n", "*Topic...	gpl-3.0
jepegit/cellpy	dev_utils/new_formats/tweaking_pec_OLD.ipynb	1	7322	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PEC" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": ...	mit
BartKeulen/drl	notebooks/RandomExploration.ipynb	1	2055627	null	mit
EmuKit/emukit	notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb	1	35713	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# External objective function evaluation in Bayesian optimization with Emukit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview" ] }, { "cell_type": "markdown", "metadata": {}, ...	apache-2.0
mdeff/ntds_2016	project/reports/breast_cancer/report.ipynb	1	12202209	null	mit
cdt15/lingam	examples/VARMALiNGAM.ipynb	1	49086	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# VARMALiNGAM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import and settings\n", "In this example, we need to import `numpy`, `pandas`, and `graphviz` in addition to `lingam`." ] }, ...	mit
mdiaz236/DeepLearningFoundations	tensorboard/Anna_KaRNNa.ipynb	1	37430	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Anna KaRNNa\n", "\n", "In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on...	mit
andypetrella/spark-notebook	notebooks/viz/Simple Plotly Chart.snb.ipynb	3	60770	{ "metadata" : { "id" : "c7f8e137-ece9-4383-b2c9-6604fe3b072c", "name" : "Simple Plotly Chart", "user_save_timestamp" : "1111-10-12T09:00:00.000Z", "auto_save_timestamp" : "1970-01-01T03:00:00.000Z", "language_info" : { "name" : "scala", "file_extension" : "scala", "codemirror_mo...	apache-2.0
SarahWooller/Using_NetSurfP	Using_NetSurfP/NetsurfP pipeline.ipynb	2	24406	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NetsurfP pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Intention is to take a list of mutations and\n", "make a file for netsurfp\n", "\n", "for each of the mutations\n", "...	mit
tensorflow/docs-l10n	site/ja/lite/performance/post_training_integer_quant_16x8.ipynb	1	19418	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "c8Cx-rUMVX25" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...	apache-2.0
dadavidson/Python_Lab	Complete-Python-Bootcamp/Lambda expressions.ipynb	2	8317	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#lambda expressions\n", "\n", "One of Pythons most useful (and for beginners, confusing) tools is the lambda expression. lambda expressions allow us to create \"anonymous\" functions. This basically means we can quickly make ad...	mit
ipython/ipywidgets	docs/source/examples/Index.ipynb	1	2449	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Back to the main [Index](../Index.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython ...	bsd-3-clause
GoogleCloudPlatform/vertex-ai-samples	notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb	1	78486	{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "id": "copyright" }, "outputs": [], "source": [ "# Copyright 2021 Google LLC\n", "#\n", "# Licensed under the Apache License, Version 2.0 (the \"License\");\n", "#...	apache-2.0
telescopeuser/uat_shl	rnd03/shl_sm_NoOCR_v010.ipynb	1	433077	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### SHL Project\n", "\n", "* simulation module: shl_sm\n", "\n", "### shl_sm required data feeds:\n", "\n", "* live bidding price, per second, time series\n", "\n", "### prediction module parameters/csv\...	mit
joosthub/pytorch-nlp-tutorial-sf2017	day_1/4_Chinese_document_classification.ipynb	1	58545	{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Chinese document Classification" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true },...	mit
jnarhan/Breast_Cancer	src/models/JN_BC_Threshold_Diagnosis.ipynb	1	282739	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<hr>\n", "<h1>Predicting Benign and Malignant Classes in Mammograms Using Thresholded Data</h1>\n", "\n", "<p>Jay Narhan</p>\n", "June 2017\n", "\n", "This is an application of the best performing models but usi...	mit
chapmanbe/RadNLP	notebooks/radnlp_demo.ipynb	1	18601	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# RadNLP\n", "## Radiology NLP or\n", "## Rad (as in cool) NLP or\n", "## [Fill in the Blank] NLP\n", "#### © Brian E. Chapman, PhD\n", "\n", "RadNLP is a package that builds upon the [pyConTextNLP]()...	apache-2.0
sains1/UTRonsProject	Notebooks/11_microRNAOverlap.ipynb	1	33611	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Finding the overlap between novel UTRons and microRNA sites predicted from TargetScan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Statement to find overlap: \n", "\n", "bedtools interse...	mit
asazo/ANN	tarea3/pregunta1.ipynb	2	1107276	null	mit
DistrictDataLabs/03-censusables	censusables/data/starbucks-location-frequencies.ipynb	1	68483	{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "A look at Starbucks Location Distribution\n", "======================\n", "\n", "When looking at block data, I noticed that very few geographic units have more than one St...	apache-2.0
tpin3694/tpin3694.github.io	python/csv_to_python_code.ipynb	1	33214	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Convert A CSV Into Python Code To Recreate It \n", "Slug: csv_to_python_code \n", "Summary: Convert A CSV Into Python Code To Recreate It \n", "Date: 2016-05-01 12:00 \n", "Category: Python \n", "Tags: D...	mit
google/jax	docs/notebooks/Common_Gotchas_in_JAX.ipynb	1	61852	{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "hjM_sV_AepYf" }, "source": [ "# 🔪 JAX - The Sharp Bits 🔪\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/notebooks/Comm...	apache-2.0
jhamilius/chain	notebooks/chain-clustering.ipynb	1	241148	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Clustering and outlier detection\n", "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<u>Objectives</u>\n", "- Test clustering methods on the features extracted from the graph for nodes ...	mit
ES-DOC/esdoc-jupyterhub	notebooks/csiro-bom/cmip6/models/sandbox-1/landice.ipynb	1	37256	{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Landice \n", "MIP Era: CMIP6 \n", "Institute: CSIRO-BOM \n", "Source ID: SANDBOX-1 \n", "...	gpl-3.0
landlab/landlab	notebooks/tutorials/agent_based_modeling/groundwater/landlab_mesa_groundwater_pumping.ipynb	1	17944	{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coupling a Landlab groundwater with a Mesa agent-based model\n", "\n", "This notebook shows a toy example of how one might couple a simple groundwater model (Landlab's `GroundwaterDupuitPercolator`, by [Litwin et al. (2020)](...	mit
joshnsolomon/phys202-2015-work	assignments/assignment05/InteractEx04.ipynb	1	18375	{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Interact Exercise 4" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 39, "metad...	mit

sauloal/ipython

probes/probes_SL2.40sc04878.ipynb

1

587888

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Parameters" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Column Names\n...

mit

dsacademybr/PythonFundamentos

Cap05/Notebooks/DSA-Python-Cap05-02-Objetos.ipynb

1

8486

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# <font color='blue'>Data Science Academy - Python Fundamentos - Capítulo 5</font>\n", "\n", "## Download: http://github.com/dsacademybr" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "out...

gpl-3.0

dinkelk/PyDyGraphs

examples/PyDyGraphTester.ipynb

1

388517

{ "cells": [ { "cell_type": "code", "execution_count": 35, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Import pydygraphs and numpy\n", "import sys\n", "sys.path.append(\"../\")\n", "\n", "import dygraphs.graph as dy\n", "import numpy as np" ] },...

mit

tschinz/iPython_Workspace

01_Mine/MachineLearning/tensorflow-examples_nb/2_BasicModels/gradient_boosted_decision_tree.ipynb

3

32353

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Gradient Boosted Decision Tree\n", "\n", "Implement a Gradient Boosted Decision tree (GBDT) with TensorFlow to classify\n", "handwritten digit images. This example is using the MNIST database of\n", "handwritten digit...

gpl-2.0

marcinofulus/ProgramowanieRownolegle

CUDA/iCSE_PR_map2d.ipynb

2

137134

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Zastosowanie indeksowania wielowymiarowego\n", "\n", "\n", "Zadanie: Oblicz wartości funkcji $\\sin(x^2+y^2)$ na siatce w zadanym obszarze.\n", "\n", "### Krok pierwszy\n", "\n", "Napiszemy jądro obliczaj...

gpl-3.0

ual/hedonic-models

sales-hedonic.ipynb

1

28218

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Tutorial on Hedonic Regression\n", "\n", "This material uses Python to demonstrate some aspects of hedonic regression, but the objective here is not to learn to program, but to understand the hedonic regression methodology. ...

bsd-3-clause

btw2111/intro-numerical-methods

0_intro_numerical_methods.ipynb

8

82547

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "slide" } }, "source": [ "# Introduction and Motivation: Modeling and methods for scientific computing" ] }, { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "sli...

mit

computational-class/computational-communication-2016

qinqiang/homework3/homework_3.ipynb

1

13080

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "吸大麻真的不会上瘾吗？High一下的代价比你想象得大\n", "2016-04-20\n", "\n", "微信ID:ibookreview『每个早晨，与534000人一起阅读』据说，...

mit

matthewzimmer/traffic-sign-classification

Traffic_Signs_Recognition_Cifar10.ipynb

1

2515092

null

mit

hande-qmc/hande

tools/pyhande/tutorials/3_custom_get_results_ccmc.ipynb

1

8243

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This demonstration shows how CCMC [1] data (analysis) results can be analysed in a more customised way. \n", "This applies to FCIQMC [2] as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": {},...

lgpl-2.1

PYPIT/PYPIT

doc/comparisons/PYPIT_vs_LowRedux_Kastb.ipynb

1

1350742

null

gpl-3.0

nmiculinic/psiml2017-facs

TestSpredSheet.ipynb

1

2720

{ "cells": [ { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from openpyxl import load_workbook\n", "import os" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": true }, "ou...

mit

ComputationalModeling/spring-2017-danielak

past-semesters/fall_2016/day-by-day/day15-Schelling-1-dimensional-segregation-day2/Day_15_Pre_Class_Notebook.ipynb

1

5010

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Getting ready to implement the Schelling model\n", "\n", "## Goal for this assignment\n", "\n", "The goal of this assignment is to finish up the two functions that you started in class on the first day of this project...

agpl-3.0

andypetrella/spark-notebook

notebooks/core/Spark-101.snb.ipynb

5

13224

{ "metadata" : { "name" : "Spark 101", "user_save_timestamp" : "1970-01-01T01:00:00.000Z", "auto_save_timestamp" : "2015-01-10T00:02:12.659Z", "language_info" : { "name" : "scala", "file_extension" : "scala", "codemirror_mode" : "text/x-scala" }, "trusted" : true, "custom...

apache-2.0

minesh1291/Practicing-Kaggle

MNIST_2017/dump_/talking_keras_notebook.ipynb

1

16708

{ "cells": [ { "metadata": { "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19", "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5", "trusted": false, "collapsed": true }, "cell_type": "code", "source": "# This Python 3 environment comes with many helpful...

gpl-3.0

UWSEDS/LectureNotes

Fall2018/09_UnitTests/unit-tests.ipynb

1

17995

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Unit Tests" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "...

bsd-2-clause

xmnlab/notebooks

jupyter/Introducción.ipynb

1

66814

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

mit

kgourgou/set-phasers-to-stan

birth-death-paths-stan/.ipynb_checkpoints/paths-checkpoint.ipynb

1

2375

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from matplotlib.pyplot import plot\n", "from ssa import SSA\n", "from pystan import stan\n", "\n", "%matplotlib inline" ] }, { "cell_type": "co...

mit

yl565/statsmodels

examples/notebooks/ols.ipynb

3

12634

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Ordinary Least Squares" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "from __future__ impor...

bsd-3-clause

kiaderouiche/hilbmetrics

notebooks/convexGeometry.ipynb

2

1108

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## This IPython notebook contains the example code presented symbolic calculs in Convex Hull " ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%matplotlib inlin...

apache-2.0

margulies/gradient_analysis

06_supplementary_analysis_and_figures.ipynb

2

82762

{ "cells": [ { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Components variance" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", ...

mit

queirozfcom/python-sandbox

python3/notebooks/pandas-columns/main.ipynb

1

65323

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## accompanying blog post: http://queirozf.com/entries/pandas-dataframe-examples-column-operations" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plai...

mit

ssunkara1/bqplot

examples/Advanced Plotting/Animations.ipynb

1

36869

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "### Animations of marks and axes can be enabled by setting 'animation_duration' property of the figure" ] }, { "cell_type": "markdown", "metadata": { "deletable": true, ...

apache-2.0

marioberges/F16-12-752

lectures/Lecture2.ipynb

1

14519

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Lecture \\#2: Setting up your Development Environment" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here is what I intend to cover today:\n", "\n", "* Python Basics\n", "* What is Inter...

gpl-3.0

GoogleCloudPlatform/cloudml-samples

notebooks/xgboost/TrainingWithXGBoostInCMLE.ipynb

1

18649

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# XGBoost Training on AI Platform\n", "This notebook uses the [Census Income Data Set](https://archive.ics.uci.edu/ml/datasets/Census+Income) to demonstrate how to train a model on Ai Platform." ] }, { "cell_type": "markd...

apache-2.0

aksp/vrview

examples/orientations/optical-flow-to-important-points.ipynb

1

41091

{ "cells": [ { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "videos/probation-reg-small.mp4\n" ] }, { "name": "stderr", "output_type": "stream", ...

apache-2.0

mdiaz236/DeepLearningFoundations

Untitled.ipynb

1

2115

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[3.0, 3.5, 4.199999999999999]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ...

mit

bollwyvl/nosebook

tests/py3/ipy3/Scrubbing.ipynb

1

3737

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import random\n", "from IPython import display" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outpu...

bsd-3-clause

wrossmorrow/sgsb

baseball.ipynb

1

1580107

null

apache-2.0

google/jax-cfd

notebooks/ml_model_inference_demo.ipynb

1

951934

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "id": "dq5Hou4QzH0Q", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "2dec9716-08bc-457f-c937-1a00e12e13da" }, "outputs": [ { "output_t...

apache-2.0

GoogleCloudPlatform/training-data-analyst

courses/machine_learning/deepdive/01_bigquery/labs/c_extract_and_benchmark.ipynb

2

15482

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Extract Datasets and Establish Benchmark\n", "\n", "**Learning Objectives**\n", "- Divide into Train, Evaluation and Test datasets\n", "- Understand why we need each\n", "- Pull data out of BigQuery and into CSV\n...

apache-2.0

MassimoSchiappa/datascience

FinaleExamUCSD+DSE200x.ipynb

1

8779948

null

apache-2.0

lemonyhermit/CodingYoga

python-for-developers/Chapter1/Chapter1_Introduction.ipynb

1

15763

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "[Python for Developers](http://ricardoduarte.github.io/python-for-developers/#content)\n", "==========================\n", "First edition\n", "-----------------------------------\n", "\n", "Chapter 1\n", "======...

gpl-2.0

zhouqifanbdh/liupengyuan.github.io

chapter1/homework/computer/3-22/201611680345 .ipynb

54

5546

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "请输入你的姓名，以回车结束。lai\n", "请输入你的出生日期，以回车结束。6.04\n", "lai 你是双子座\n" ] } ], "source": [ ...

mit

mne-tools/mne-tools.github.io

0.14/_downloads/plot_sensors_decoding.ipynb

1

5391

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

bsd-3-clause

getmykhan/PythonforDataAnalytics

Machine Learning/Pokemon.ipynb

1

631950

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "D:\\WorkArea\\Anaconda3\\lib\\site-packages\\sklearn\\cross_validation.py:41: DeprecationWarning: This module was deprecated in version 0...

gpl-3.0

zambzamb/zpic

python/Animation.ipynb

1

5158

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Animation using direct access to simulation data\n", "\n", "In this more advanced example, we show how to create a movie of the particles phasespace using direct access to the simulation data. The initialization of the simula...

agpl-3.0

yigong/AY250

hw4/hw4_training.ipynb

1

9498

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "# import\n", "#!/usr/bin/env python\n", "import matplotlib.pyplot as plt\n", "from glob import glob\n", "impo...

mit

bamford/gzhubble

python/creating_debiased_catalog/STEP_1_p_features_thresholds_slope_method.ipynb

1

835447

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Step 1: Analyzing FERENGI Data \n", "## Determine correctable / uncorrectable regions of z/mu space. Ferengi galaxies that are considered 'correctable' are used to compute zeta in Step 2. This also generates low and hi limit valu...

mit

karlstroetmann/Artificial-Intelligence

Python/6 Classification/Polynomial-Logistic-Regression.ipynb

1

22080

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "from IPython.core.display import HTML\n", "with open (\"../style.css\", \"r\") as file:\n", " css = file.read()\n", "HTML(css)" ] }, { "cell_ty...

gpl-2.0

tpin3694/tpin3694.github.io

machine-learning/.ipynb_checkpoints/loading_scikit-learns_digits-dataset-checkpoint.ipynb

1

8545

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Loading Scikit-Learn's Digits Dataset \n", "Slug: loading_scikit-learns_digits-dataset \n", "Summary: Loading the built-in digits datasets of Scikit-Learn. \n", "Date: 2016-08-31 12:00 \n", "Category: Machin...

mit

CompPhysics/MachineLearning

doc/Programs/JupyterFiles/Examples/Housing.ipynb

1

631949

{ "cells": [ { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYYAAAEKCAYAAAAW8vJGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBo\ndHRwOi8vbWF0cGxvdGxp...

cc0-1.0

Joshuaalbert/IonoTomo

src/ionotomo/notebooks/PropagationPathlengthVar.ipynb

1

44135

{ "cells": [ { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "ename": "ImportError", "evalue": "No module named 'Logger'", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------...

apache-2.0

georgetown-analytics/yelp-classification

Yelp_web_scrapper/Review_Scrapper.ipynb

1

38663

{ "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from bs4 import BeautifulSoup\n", "import requests\n", "import re\n", "import json\n", "import sys\n", "sys.path.append('/Users/robertsonwang/Desktop...

mit

tensorflow/docs-l10n

site/en-snapshot/tfx/tutorials/tfx/penguin_simple.ipynb

1

23515

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "DjUA6S30k52h" }, "source": [ "##### Copyright 2021 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...

apache-2.0

c22n/ion-channel-ABC

docs/examples/crn/crn_ina_testing.ipynb

1

204814

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Create and test ion channel model" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "INFO:myokit:Loadin...

gpl-3.0

mdastro/Challenge

Coding/SanityCheckAll.ipynb

1

170767

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/mldantas/miniconda2/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib ...

mit

davidvilla/python-course

iteradores.ipynb

1

9956

{ "metadata": { "name": "", "signature": "sha256:aecedc9996af45fc57408c5a0e8e47ebf9a40244087ac323522ec0b9556afa64" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Creando un iterador" ] }, ...

gpl-2.0

mne-tools/mne-tools.github.io

0.17/_downloads/d25fdfa446b06c82b756855681845935/plot_mne_dspm_source_localization.ipynb

1

10262

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

bsd-3-clause

PyPSA/PyPSA

examples/notebooks/scigrid-sclopf.ipynb

2

3565

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Security-Constrained Optimisation\n", "\n", "In this example, the dispatch of generators is optimised using the security-constrained linear OPF, to guaranteed that no branches are overloaded by certain branch outages." ] ...

mit

mementum/backtrader

samples/pyfoliotest/backtrader-pyfolio.ipynb

1

1470926

null

gpl-3.0

oresat/oresat-design

SatReports.ipynb

1

34317

{ "metadata": { "name": "", "signature": "sha256:e09322625ea94dd3a92b5256503244c5520f56dc10befd459de92aa0247a46b4" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import ephem\n", "import numpy as np\...

gpl-3.0

mne-tools/mne-tools.github.io

0.12/_downloads/plot_interpolate_bad_channels.ipynb

1

2308

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

bsd-3-clause

dominikgrimm/ridge_and_svm

Toy-Example-Solution.ipynb

1

225315

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Toy Example: Ridge Regression vs. SVM\n", "<p></p>\n", " \n", "<div style=\"text-align:justify\">\n", " In this toy example we will compare two machine learning models: <em>Ridge Regression</em> and <em>C-SVM</e...

mit

erfannoury/TextClassification

sklearn-text classification.ipynb

2

112878

{ "metadata": { "name": "", "signature": "sha256:b120c5bed47d95f008362ba2a2a459b1463e2594f34b1a08044be28d28fa31df" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "import numpy as np\n", "import scipy ...

mit

ziky5/F4500_Python_pro_fyziky

lekce_05/B.ipynb

1

2557

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true }, "outputs": [], "source": [ "def B(t, r):\n", " \"\"\"Returns magnetic field of a current loop in point r = (r1, r2, r3) and time t\n", " Th...

mit

jasag/Phytoliths-recognition-system

code/notebooks/Prototypes/Image_Labeler_Prototypes/Image_Labeler_prototype2.ipynb

1

17921

{ "cells": [ { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": true }, "outputs": [], "source": [ "from __future__ import print_function\n", "import ipywidgets as widgets\n", "from traitlets import Unicode, validate, Bool, Any\n", "from ipywidgets import C...

bsd-3-clause

balarsen/pymc_learning

combine_counts/Combine counts and resample.ipynb

1

521123

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Idea for spectrogram error bars\n", "\n", "1. For each pixel get the dist that go into thwt ais plotted. \n", "1. Resample each based on the dist\n", "1. replot\n", "1. repeat and see which are still there" ] }...

bsd-3-clause

kimlaborg/NGSKit

notebooks/1-PWM_basics.ipynb

1

54824

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PWMs\n", "\n", "This notebook is a short example of how to generate a PWM from a MSA, and a few analyses you can do using Ngskit. " ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "Execute...

mit

catalystcomputing/DSIoT-Python-sessions

Session3/code/03 Supervised Learning - 00 Python basics and Logistic Regression.ipynb

1

14363

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Here we introduce Data science by starting with a common regression model(logistic regression). The example uses the Iris Dataset\n", "# We also introduce Pytho...

apache-2.0

helgako/cms-dqm

notebooks/CMS-AE.ipynb

1

1070552

null

mit

kmunve/APS

aps/notebooks/ml_varsom/linear_regression.ipynb

1

35332

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# LINEAR REGRESSION\n", "\n", "- is the simplest machine learning model\n", "- is used for finding linear relationship between target and one or more predictors\n", "- there are two types of linear regression:\n", "...

mit

alexjj/money-scripts

UK Index Prices.ipynb

1

5161

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "ename": "ModuleNotFoundError", "evalue": "No module named 'selenium'", "output_type": "error", "traceback": [ "\u001b[0;31m--------------------------------------------------------------...

bsd-2-clause

ComputationalModeling/spring-2017-danielak

past-semesters/spring_2016/day-by-day/day18-kinematics-terminal-velocity-of-a-skydiver/video_stuff/example_function.ipynb

2

1544

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook calculates an example function that we'll be using in our videos of numerical integration and differentiation!" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false ...

agpl-3.0

hdesmond/StatisticalMethods

lessons/2.UnderstandingFromData.ipynb

1

1543

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PHYS366: Statistical Methods in Astrophysics\n", "\n", "\n", "# Lesson 2: Understanding From Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Goals for this session:\n", "\n",...

gpl-2.0

leewujung/trex_fish

Split-window normalizer and total energy variation.ipynb

1

528171

{ "cells": [ { "cell_type": "code", "execution_count": 142, "metadata": { "collapsed": false }, "outputs": [], "source": [ "if isunix\n", " addpath('~/internal_2tb/Dropbox/0_CODE/MATLAB/saveSameSize');\n", " addpath(['~/internal_2tb/Dropbox/0_CODE/trex_fish/Triplet_processing_...

gpl-3.0

scottgigante/m-phate

examples/classification_keras.ipynb

1

16352780

null

gpl-3.0

DJCordhose/ai

notebooks/unsupervised/pca-intuition.ipynb

1

42188

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inli...

mit

ncfausti/machine_learning

Week 2.ipynb

1

2202

{ "metadata": { "name": "", "signature": "sha256:3af4550ebf00deb5c2ca529d7bc510bbd041151233f037119e7831e9ee591e6a" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#Week 2\n", "\n", "####Matrix ...

gpl-3.0

djangogo/CoCo-Matlab-API-on-Windows

PythonAPI/pycocoEvalDemo.ipynb

2

517049

{ "cells": [ { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from pycocotools.coco import COCO\n", "from pycocotools.cocoeval import COCOeval\n", "import...

bsd-2-clause

eds-uga/cbio4835-sp17

lectures/Lecture23.ipynb

1

2013925

null

mit

aswolf/xmeos

examples/test-RTpress-MgSiO3/06-compare-miegruneisen-EOS.ipynb

1

12414

{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib notebook\n", "import numpy as np\n", "import pandas as pd\n", "import pickle\n", "\n", "import xmeos\n", "from xmeos...

mit

sansbacon/nba

examples/machine_learning.ipynb

1

49100

{ "metadata": { "name": "", "signature": "sha256:28944676c90a8f3613d515070149629e59524ed4f81fa592b84f48df6a911150" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "%load_ext sql\n", "%matplotlib inline...

mit

nikken1/patentprocessor

notebooks/MySQL.ipynb

6

841125

{ "metadata": { "name": "" }, "nbformat": 3, "nbformat_minor": 0, "worksheets": [ { "cells": [ { "cell_type": "code", "collapsed": false, "input": [ "from sqlalchemy import create_engine, MetaData, Table, inspect\n", "from sqlalchemy.sql import select\n", "from sqlalchem...

bsd-2-clause

ES-DOC/esdoc-jupyterhub

notebooks/cas/cmip6/models/sandbox-3/ocean.ipynb

1

164407

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

gpl-3.0

jepegit/cellpy

dev_utils/new_formats/tweaking_pec_OLD.ipynb

1

7322

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# PEC" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%load_ext autoreload\n", "%autoreload 2" ] }, { "cell_type": "code", "execution_count": ...

mit

BartKeulen/drl

notebooks/RandomExploration.ipynb

1

2055627

null

mit

EmuKit/emukit

notebooks/Emukit-tutorial-bayesian-optimization-external-objective-evaluation.ipynb

1

35713

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# External objective function evaluation in Bayesian optimization with Emukit" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Overview" ] }, { "cell_type": "markdown", "metadata": {}, ...

apache-2.0

mdeff/ntds_2016

project/reports/breast_cancer/report.ipynb

1

12202209

null

mit

cdt15/lingam

examples/VARMALiNGAM.ipynb

1

49086

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# VARMALiNGAM" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Import and settings\n", "In this example, we need to import `numpy`, `pandas`, and `graphviz` in addition to `lingam`." ] }, ...

mit

mdiaz236/DeepLearningFoundations

tensorboard/Anna_KaRNNa.ipynb

1

37430

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Anna KaRNNa\n", "\n", "In this notebook, I'll build a character-wise RNN trained on Anna Karenina, one of my all-time favorite books. It'll be able to generate new text based on...

mit

andypetrella/spark-notebook

notebooks/viz/Simple Plotly Chart.snb.ipynb

3

60770

{ "metadata" : { "id" : "c7f8e137-ece9-4383-b2c9-6604fe3b072c", "name" : "Simple Plotly Chart", "user_save_timestamp" : "1111-10-12T09:00:00.000Z", "auto_save_timestamp" : "1970-01-01T03:00:00.000Z", "language_info" : { "name" : "scala", "file_extension" : "scala", "codemirror_mo...

apache-2.0

SarahWooller/Using_NetSurfP

Using_NetSurfP/NetsurfP pipeline.ipynb

2

24406

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# NetsurfP pipeline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Intention is to take a list of mutations and\n", "make a file for netsurfp\n", "\n", "for each of the mutations\n", "...

mit

tensorflow/docs-l10n

site/ja/lite/performance/post_training_integer_quant_16x8.ipynb

1

19418

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "c8Cx-rUMVX25" }, "source": [ "##### Copyright 2020 The TensorFlow Authors." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "cellView": "form", "i...

apache-2.0

dadavidson/Python_Lab

Complete-Python-Bootcamp/Lambda expressions.ipynb

2

8317

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "#lambda expressions\n", "\n", "One of Pythons most useful (and for beginners, confusing) tools is the lambda expression. lambda expressions allow us to create \"anonymous\" functions. This basically means we can quickly make ad...

mit

ipython/ipywidgets

docs/source/examples/Index.ipynb

1

2449

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Back to the main [Index](../Index.ipynb)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Interactive Widgets" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "IPython ...

bsd-3-clause

GoogleCloudPlatform/vertex-ai-samples

notebooks/community/migration/UJ2,12 Custom Training Prebuilt Container TF Keras.ipynb

1

78486

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

apache-2.0

telescopeuser/uat_shl

rnd03/shl_sm_NoOCR_v010.ipynb

1

433077

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### SHL Project\n", "\n", "* simulation module: shl_sm\n", "\n", "### shl_sm required data feeds:\n", "\n", "* live bidding price, per second, time series\n", "\n", "### prediction module parameters/csv\...

mit

joosthub/pytorch-nlp-tutorial-sf2017

day_1/4_Chinese_document_classification.ipynb

1

58545

{ "cells": [ { "cell_type": "markdown", "metadata": { "deletable": true, "editable": true }, "source": [ "# Chinese document Classification" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true, "deletable": true, "editable": true },...

mit

jnarhan/Breast_Cancer

src/models/JN_BC_Threshold_Diagnosis.ipynb

1

282739

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "<hr>\n", "<h1>Predicting Benign and Malignant Classes in Mammograms Using Thresholded Data</h1>\n", "\n", "<p>Jay Narhan</p>\n", "June 2017\n", "\n", "This is an application of the best performing models but usi...

mit

chapmanbe/RadNLP

notebooks/radnlp_demo.ipynb

1

18601

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# RadNLP\n", "## *Rad*iology NLP or\n", "## *Rad* (as in cool) NLP or\n", "## *[Fill in the Blank]* NLP\n", "#### © Brian E. Chapman, PhD\n", "\n", "RadNLP is a package that builds upon the [pyConTextNLP]()...

apache-2.0

sains1/UTRonsProject

Notebooks/11_microRNAOverlap.ipynb

1

33611

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Finding the overlap between novel UTRons and microRNA sites predicted from TargetScan" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Statement to find overlap: \n", "\n", "bedtools interse...

mit

asazo/ANN

tarea3/pregunta1.ipynb

2

1107276

null

mit

DistrictDataLabs/03-censusables

censusables/data/starbucks-location-frequencies.ipynb

1

68483

{ "cells": [ { "cell_type": "markdown", "metadata": { "slideshow": { "slide_type": "-" } }, "source": [ "A look at Starbucks Location Distribution\n", "======================\n", "\n", "When looking at block data, I noticed that very few geographic units have more than one St...

apache-2.0

tpin3694/tpin3694.github.io

python/csv_to_python_code.ipynb

1

33214

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Title: Convert A CSV Into Python Code To Recreate It \n", "Slug: csv_to_python_code \n", "Summary: Convert A CSV Into Python Code To Recreate It \n", "Date: 2016-05-01 12:00 \n", "Category: Python \n", "Tags: D...

mit

google/jax

docs/notebooks/Common_Gotchas_in_JAX.ipynb

1

61852

{ "cells": [ { "cell_type": "markdown", "metadata": { "id": "hjM_sV_AepYf" }, "source": [ "# 🔪 JAX - The Sharp Bits 🔪\n", "\n", "[![Open in Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google/jax/blob/main/docs/notebooks/Comm...

apache-2.0

jhamilius/chain

notebooks/chain-clustering.ipynb

1

241148

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Clustering and outlier detection\n", "<hr>" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "<u>Objectives</u>\n", "- Test clustering methods on the features extracted from the graph for nodes ...

mit

ES-DOC/esdoc-jupyterhub

notebooks/csiro-bom/cmip6/models/sandbox-1/landice.ipynb

1

37256

{ "nbformat_minor": 0, "nbformat": 4, "cells": [ { "source": [ "# ES-DOC CMIP6 Model Properties - Landice \n", "**MIP Era**: CMIP6 \n", "**Institute**: CSIRO-BOM \n", "**Source ID**: SANDBOX-1 \n", "...

gpl-3.0

landlab/landlab

notebooks/tutorials/agent_based_modeling/groundwater/landlab_mesa_groundwater_pumping.ipynb

1

17944

{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Coupling a Landlab groundwater with a Mesa agent-based model\n", "\n", "This notebook shows a toy example of how one might couple a simple groundwater model (Landlab's `GroundwaterDupuitPercolator`, by [Litwin et al. (2020)](...

mit

joshnsolomon/phys202-2015-work

assignments/assignment05/InteractEx04.ipynb

1

18375

{ "cells": [ { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "# Interact Exercise 4" ] }, { "cell_type": "markdown", "metadata": { "nbgrader": {} }, "source": [ "## Imports" ] }, { "cell_type": "code", "execution_count": 39, "metad...

mit