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 |
|---|---|---|---|---|---|
kwinkunks/rainbow | notebooks/Guessing_colourmaps-MPLIMAGE-DUPECOL.ipynb | 1 | 4242843 | null | apache-2.0 |
Seek/ValueTrackerQT | Untitled.ipynb | 1 | 5336 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sqlalchemy import Column, Integer, String, ForeignKey\n",
"from sqlalchemy.orm import relationship, backref\n",
"\n",
"from sqlalchemy.ext.associationpro... | gpl-3.0 |
alvaroing12/CADL | session-2/lecture-2.ipynb | 3 | 2760808 | null | apache-2.0 |
mne-tools/mne-tools.github.io | dev/_downloads/8b7a85d4b98927c93b7d9ca1da8d2ab2/compute_mne_inverse_volume.ipynb | 1 | 3014 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n\n# Compute MN... | bsd-3-clause |
tomchor/pymicra | publications/agu2017/pprog/all_poster.ipynb | 2 | 193277 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"\\begin{exampleblock}{Preamble}"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pymicra\n",
"import pandas as pd\n",
"from glob import glob\n",
"imp... | gpl-3.0 |
JasonSanchez/w261 | week11/MIDS-W261-HW-11-Sanchez.ipynb | 1 | 327022 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# MIDS - w261 Machine Learning At Scale\n",
"__Course Lead:__ Dr James G. Shanahan (__email__ Jimi via James.Shanahan _AT_ gmail.com)\n",
"\n",
"## Assignment - HW11\n",
"\n",
"\n",
"---\n",
"__Name:__ *Ja... | mit |
ifduyue/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers | Chapter1_Introduction/Ch1_Introduction_PyMC3.ipynb | 2 | 369658 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Probabilistic Programming\n",
"=====\n",
"and Bayesian Methods for Hackers \n",
"========\n",
"\n",
"##### Version 0.1\n",
"\n",
"`Original content created by Cam Davidson-Pilon`\n",
"\n",
"`Ported t... | mit |
GoogleCloudPlatform/training-data-analyst | courses/machine_learning/deepdive2/image_understanding/solutions/Keras_Flowers_on_TPU.ipynb | 1 | 2298241 | null | apache-2.0 |
miykael/nipype_tutorial | notebooks/basic_iteration.ipynb | 1 | 11605 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Iterables\n",
"\n",
"Some steps in a neuroimaging analysis are repetitive. Running the same preprocessing on multiple subjects or doing statistical inference on multiple files. To prevent the creation of multiple individual s... | bsd-3-clause |
djvanhelmond/AdventofCode2015 | Day 14/Day 14 Reindeer Olympics.ipynb | 1 | 2723 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#!/usr/bin/env python3"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
... | mit |
jamesfolberth/NGC_STEM_camp_AWS | notebooks/data8_notebooks/lab01/lab01.ipynb | 2 | 34839 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lab 1: Expressions\n",
"\n",
"\n",
"#### Today's lab\n",
"\n",
"In today's lab, you'll learn how to:\n",
"\n",
"1. navigate Jupyter notebooks (like this one);\n",
"2. write and evaluate some basic *exp... | bsd-3-clause |
jacobstein123/Flappy-Bird-Genetic-Algorithm | Misc data/LearningCurve.ipynb | 1 | 2354 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"with open(\"output-10.31.15.txt\",'r') as output:\n",
" a = output.read()"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"co... | mit |
fonnesbeck/scipy2015_tutorial | notebooks/1. Data Preparation.ipynb | 6 | 297198 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
" @font-face {\n",
" font-family: \"Computer Modern\";\n",
" src: url('http://mirrors.ctan.... | cc0-1.0 |
dikien/break-captcha | Step4_Generate_Clssifier_sknn_increase_units.ipynb | 1 | 509346 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from matplotlib import pyplot as plt\n",
"from sklearn.grid_search import GridSearchCV\n",
"from sklearn.cross_validation import train... | bsd-2-clause |
rnder/data-science-from-scratch | notebook/ch16_logistic_regression.ipynb | 1 | 89285 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"# Ch.16 - Logistic Regression"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"deletable": true,
"e... | unlicense |
wehlutyk/brainscopypaste | data/notebooks/Model(time=Time.discrete, source=Source.majority, past=Past.last_bin, durl=Durl.exclude_past, max_distance=1) - variation.ipynb | 1 | 3551235 | null | gpl-3.0 |
dhpollack/programming_notebooks | pymc-test.ipynb | 1 | 226659 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import pymc\n",
"import numpy as np\n",
"import pandas as pd\n",
"import seaborn as sns"
]
},
{
"cell_type": "code",
"execution_count": 32,
"... | mit |
c-martinez/w2vExample | index.ipynb | 1 | 107442 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Download data from [google](https://drive.google.com/file/d/0B7XkCwpI5KDYNlNUTTlSS21pQmM/edit?usp=sharing) or it's [mirror a](https://s3.amazonaws.com/mordecai-geo/GoogleNews-vectors-nega... | apache-2.0 |
yigong/AY250 | hw3/hw3-2.ipynb | 1 | 4229 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib\n",
"tp = [('date','i8'),('value','f8')]\n... | mit |
richardotis/pycalphad-fitting | Al-Ni/Al-Ni-L12-Results.ipynb | 1 | 1788910 | null | mit |
tuanavu/coursera-university-of-washington | machine_learning/1_machine_learning_foundations/assignment/week3/Analyzing product sentiment.ipynb | 1 | 557233 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Predicting sentiment from product reviews\n",
"\n",
"#Fire up GraphLab Create"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"... | mit |
NSLS-II-HXN/PyXRF | examples/Spectrum_plot_with_given_area.ipynb | 3 | 662072 | {
"cells": [
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib notebook\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import h5py"
]
},
{
"cell_type": "code",
"execution_coun... | bsd-3-clause |
leewujung/trex_fish | Compare E_max, total energy, and SI for all runs.ipynb | 1 | 614936 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"run_num_all = [79,87,94,103,115,124,131];\n",
"ping_num{1} = 4:9:877; % run 79\n",
"ping_num{2} = 2:2:1000; % run 87\n",
"ping_num{3} = 3:3:899; % ru... | gpl-3.0 |
dennissergeev/classcode | notebooks/satelliteII.ipynb | 1 | 8311 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reading MODIS level 1b data in netcdf format"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here is a walkthrough of how to read metadata and data from a level1b file. I've used the the converter ... | cc0-1.0 |
AstroHuntsman/POCS | examples/notebooks/Mongo Query Example.ipynb | 1 | 242227 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from pocs.utils.database import PanMongo\n",
"from astropy.time import Time\n",
"import pandas as pd\n",
"\n",
"from matplotlib import pyplot as plt\n",
... | mit |
mobeets/blog | _notebooks/testing.ipynb | 2 | 368823 | {
"metadata": {
"name": "",
"signature": "sha256:06923e8dcf0e8debc4f9f777db0dbd032f2ade4836353ff0404d943b34236edc"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_Adapted from MATLAB code by [George Mathe... | mit |
gfabieno/SeisCL | docs/notebooks/Inversion/ComputingGradient.ipynb | 1 | 615 | {
"cells": [
{
"cell_type": "markdown",
"id": "filled-needle",
"metadata": {},
"source": [
"# Computing the Gradient\n",
"\n",
"Under Development ..."
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_in... | gpl-3.0 |
tpin3694/tpin3694.github.io | machine-learning/encoding_ordinal_categorical_features.ipynb | 2 | 5753 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Encoding Ordinal Categorical Features \n",
"Slug: encoding_ordinal_categorical_features \n",
"Summary: How to encode ordinal categorical features for machine learning in Python. \n",
"Date: 2016-09-06 12:00 \n",... | mit |
sophie63/FlyLFM | Notebooks/.ipynb_checkpoints/986_FlyLFMpaper-checkpoint.ipynb | 1 | 2222126 | null | bsd-2-clause |
UChicagoPhysics/SampleExercises | exercises/classicalMechanics/Velocity of Moving Object Under Decelerating Force.ipynb | 1 | 67218 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Velocity of Moving Object Under Decelerating Force\n",
"\n",
"- **PROGRAM**: Velocity of moving object under decelerating force\n",
"- **CREATED**: 4/14/2018"
]
},
{
"cell_type": "code",
"execution_count": 1,... | gpl-2.0 |
xpharry/Udacity-DLFoudation | tutorials/sentiment_network/.ipynb_checkpoints/Sentiment Classification - How to Best Frame a Problem for a Neural Network (Project 4)-checkpoint.ipynb | 19 | 324875 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sentiment Classification & How To \"Frame Problems\" for a Neural Network\n",
"\n",
"by Andrew Trask\n",
"\n",
"- **Twitter**: @iamtrask\n",
"- **Blog**: http://iamtrask.github.io"
]
},
{
"cell_type": "m... | mit |
bbglab/adventofcode | 2016/ferran/day7.ipynb | 1 | 4536 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Chellenge 7\n",
"\n",
"## Challenge 7.1"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"myinput = ... | mit |
scott-maddox/obpds-binder | examples/degenerate_pn_diode.ipynb | 1 | 33651 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from ipywidgets import interactive, fixed\n",
"from obpds import *\n",
"\n",
"layers = [\n",
" Layer(1*um, InAs, 1e15/cm3),... | agpl-3.0 |
zhouqifanbdh/liupengyuan.github.io | chapter2/homework/computer/3-29/201611690595-322-ex4.ipynb | 27 | 1620 | {
"cells": [
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"请输入要输入的整数个数，以回车结束：3\n",
"请输入一个整数，以回车结束：2\n",
"请输入一个整数，以回车结束：4\n",
"请输入一个整数，以回车结束：3\n",
... | mit |
arve0/FY3490 | morphology-gw-chap9/tophat.ipynb | 1 | 1434684 | null | mit |
freedomtan/tensorflow | tensorflow/lite/g3doc/performance/post_training_integer_quant_16x8.ipynb | 7 | 18889 | {
"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 |
zkbt/timingtransits | SuperSeriousNotebookForRealDataAndStuff-Backup.ipynb | 1 | 276956 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#This is a new notebook for use with real data. We totally won't try to divide by zero this time."
]
},
{
"cell_type": "code",
"execution_count": 2,
"meta... | mit |
PaulSoderlind/JuliaTutorial | Tutorial_03b_DataContainers.ipynb | 1 | 22604 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Data Containers\n",
"\n",
"This notebook shows how to combine data into different types of \"containers\" (arrays, dictionaries, tuples, ...) inside your program. \n",
"\n",
"(This notebook does not discuss DataFrames... | mit |
vadim-ivlev/STUDY | coding/Cracking the code interview.ipynb | 1 | 14856 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**1.1 Is Unique**: Implement an algorithm to determine if a string has all unique characters. What if you cannot use additional data structures?"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"ou... | mit |
ricklupton/ipysankeywidget | examples/Linking and Layout.ipynb | 1 | 31782 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Linking and Layout\n",
"\n",
"> [<i class=\"fa fa-2x fa-paper-plane text-info fa-fw\"> </i> Simple example](./Simple%20example.ipynb)\n",
">\n",
"> [<i class=\"fa fa-2x fa-space-shuttle text-info fa-fw\"> </i> Advance... | mit |
sudhanshuptl/Fun-Tool | Learn/ExtractEmail_&_remove_from databse.ipynb | 1 | 8214 | {
"metadata": {
"name": "",
"signature": "sha256:53e9c7993b1a8d16f3842da2579de65e4efb6075ec0672338f97a4f9d397c9be"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Extract Email from delevir... | gpl-2.0 |
slock83/FaceDetect | NN Playground-VariX.ipynb | 1 | 409067 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Neural network \"playground\"\n",
"\n",
"## imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"ExecuteTime": {
"end_time": "2016-11-19T18:35:49.070000",
"start_time": "2016... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.16/_downloads/plot_tf_dics.ipynb | 1 | 6648 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Time-frequen... | bsd-3-clause |
catalystcomputing/DSIoT-Python-sessions | Session5/code/09 Pandas - Part 2.ipynb | 1 | 9867 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Air quality time series\n",
"\n",
"\n",
"## Let us first describe the data\n",
"\n",
"This dataset is made of the daily readings of the following air quality values for May 1, 1973 (a Tuesday) to September 30, 197... | apache-2.0 |
guidj/xlab | lab/datio/_1.- Quijote.ipynb | 2 | 55257 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"number of lines in file: 5537\n"
]
}
],
"source": [
"import re\n",
"from operator impor... | apache-2.0 |
iABC2XYZ/abc | small/Untitled.ipynb | 1 | 2476 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
... | gpl-3.0 |
SyrakuShaikh/python | learning/fluent_python_2015/Given a test score, return the corresponding letter grade.ipynb | 1 | 1472 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import bisect"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"def grade... | gpl-3.0 |
PMEAL/OpenPNM | examples/reference/uncategorized/overview_of_domain_syntax.ipynb | 1 | 64990 | {
"cells": [
{
"cell_type": "markdown",
"id": "c833e469",
"metadata": {},
"source": [
"# OpenPNM Version 3: The new @domain syntax "
]
},
{
"cell_type": "markdown",
"id": "58dbe0a2",
"metadata": {},
"source": [
"The latest version of OpenPNM includes a new syntax feature with ... | mit |
jasag/Phytoliths-recognition-system | code/notebooks/Prototypes/Classifiers_and_HoG/1_HoG.ipynb | 1 | 7568 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Características de HoG\n",
"\n",
"En este notebook creará un conjunto de imagenes con caras y no caras mediante las que obtendremos las características de HoG que nos servirán como conjunto de entrenamiento para nuestro clasi... | bsd-3-clause |
amuesing1/spectrum | spectrum_display.ipynb | 1 | 4407 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"... | apache-2.0 |
ceos-seo/data_cube_notebooks | notebooks/training/ardc_training/Training_TaskB_Water.ipynb | 1 | 25295 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"execution": {
"iopub.execute_input": "2020-09-29T01:00:32.377279Z",
"iopub.status.busy": "2020-09-29T01:00:32.376860Z",
"iopub.status.idle": "2020-09-29T01:00:32.379296Z",
"shell.execute_reply": "2020-09-29T0... | apache-2.0 |
tiagoantao/abjad-ipython | notebooks/Ligeti - Desordre.ipynb | 1 | 48953 | {
"metadata": {
"name": "",
"signature": "sha256:26395eb934ae63f36b097fd8eb91dae7f5397b03ac8b9f07771eb9053155c424"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from abjad import *\n",
"%load_ext ab... | cc0-1.0 |
metpy/MetPy | v1.1/_downloads/324acb7faa1ec1d6ac5849ea2223364d/Smoothing.ipynb | 1 | 3793 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n# Smoothing\n\... | bsd-3-clause |
bsnacks000/IPySigma-Demo | DATA661_final.ipynb | 1 | 9743 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Final - DATA 661\n",
" \n",
"## IPySigma Lives: Presenting an Extensible Prototype Network Visualization Frontend for Jupyter Notebooks\n",
"\n",
"### John DeBlase, Daina Bouquin\n",
" \n",
"#### Introdu... | mit |
c22n/ion-channel-ABC | docs/examples/hl1/hl-1_extra-fullABC.ipynb | 1 | 2995697 | null | gpl-3.0 |
tensorflow/swift | docs/site/tutorials/Swift_autodiff_sharp_edges.ipynb | 1 | 17868 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Swift_autodiff_sharp_edges.ipynb",
"provenance": []
},
"kernelspec": {
"name": "swift",
"display_name": "Swift"
}
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id":... | apache-2.0 |
GoogleCloudPlatform/vertex-ai-samples | notebooks/community/ml_ops/stage2/get_started_vertex_training_sklearn.ipynb | 1 | 45449 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"id": "copyright"
},
"outputs": [],
"source": [
"# Copyright 2022 Google LLC\n",
"#\n",
"# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
"#... | apache-2.0 |
basnijholt/holoviews | examples/user_guide/04-Style_Mapping.ipynb | 1 | 24839 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Style Mapping"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import holoviews as hv\n",
"from holoviews import dim, opts\n",... | bsd-3-clause |
yhat/ggplot | docs/how-to/Plotting two variables as lines on the same graph.ipynb | 1 | 118223 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from ggplot import *\n",
"import pandas as pd"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plotti... | bsd-2-clause |
CompPhysics/MachineLearning | doc/Programs/JupyterFiles/Examples/Youtube Tutorials/TensorFlow Tutorial Sentdex.ipynb | 1 | 2238 | {
"cells": [
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Tensor(\"Mul_11:0\", shape=(4,), dtype=int32)\n",
"[ 5 12 21 32]\n",
"[ 5 12 21 32]\n",
"30\n",
"30\n",
"30... | cc0-1.0 |
ljubisap/ml-dojo-part-I | Do it yourself.ipynb | 1 | 10478 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"# Do it yourself..."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"## Python basi... | apache-2.0 |
AssembleSoftware/IoTPy | examples/SlidingWindows.ipynb | 1 | 6886 | {
"cells": [
{
"cell_type": "markdown",
"id": "a5025450",
"metadata": {},
"source": [
"# Sliding Windows\n",
"\n",
"Many applications require computations on sliding windows of streams. A sliding window is specified by a window size and a step size, both of which are positive integers. Slidin... | bsd-3-clause |
tpin3694/tpin3694.github.io | python/lambda_functions.ipynb | 1 | 1890 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Title: Lambda Functions \n",
"Slug: lambda_functions \n",
"Summary: Lambda Functions \n",
"Date: 2016-05-01 12:00 \n",
"Category: Python \n",
"Tags: Basics \n",
"Authors: Chris Albon "
]
},
{
"c... | mit |
catalyst-cooperative/pudl | notebooks/work-in-progress/make_master_unit_list_eia.ipynb | 1 | 13571 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recreating the Master Unit List"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### setup/imports"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs":... | mit |
ManyBodyPhysics/Course2ManyBodyMethods | doc/src/fci/fci.ipynb | 2 | 54643 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- Slides for PHY981 -->\n",
"<!-- dom:TITLE: Nuclear Shell Model -->\n",
"# Nuclear Shell Model\n",
"<!-- dom:AUTHOR: [Morten Hjorth-Jensen](http://computationalphysics.no), National Superconducting Cyclotron Laboratory ... | cc0-1.0 |
hotfuzzy/go | Reading_csv_2_batches.ipynb | 1 | 3779 | {
"metadata": {
"name": "",
"signature": "sha256:cbcf9b2fef76537eccb9b989ad5a4b1a0a0e2b747b98818ab0a02d7c4d46ae56"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import tensorflow as tf\n",
"import n... | mit |
sebastien17/MAVlink_plug | example/Test_MessageType.ipynb | 1 | 3221 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Importing ardupilotmega\n"
]
}
],
"source": [
"import mavlinkplug.Message"
]
},
{
... | bsd-3-clause |
NoonienSoong/Data-Science-45min-Intros | Bokeh/04 - styling.ipynb | 7 | 1759762 | null | unlicense |
immersinn/ncga | notebooks/explore/Extract Content from Bill Text.ipynb | 1 | 80703 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"'\\nSee \"bill_text_preproc.py\" file\\n\\n'"
]
},
"execution_count": 1,
"metadata": {},
"output_type": "execute_r... | mit |
m-weigand/ccd_tools | playground/Sensitivities/Debye Sensitivities.ipynb | 1 | 105344 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from NDimInv.plot_helper import *\n",
"import dd_res as DD_RES\n",
"dd_res = DD_RES.dd_resistivity()\n",
"import sc... | gpl-3.0 |
trangel/Data-Science | .ipynb_checkpoints/MNIST-dimensionality-reduction-checkpoint.ipynb | 1 | 430802 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"Let's use dimensionality reduction in a real example.\n",
"We will examine the MNIST database of digits, which has a large number of features 28x28. We reduce the dimensionality of our dataset to then use ou... | gpl-3.0 |
shareactorIO/pipeline | source.ml/jupyterhub.ml/notebooks/zz_old/TensorFlow/TFLearn/AdversarialWorkshop/Adversarial Workshop - Dropout Classification.ipynb | 3 | 98408 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/ipolosukhin/projects/tf_examples/.env/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserW... | apache-2.0 |
ozanarkancan/navigation | inotes/nr.ipynb | 1 | 3626 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"StatsBase.WeightVec{Float64,Array{Float64,1}}([0.319088,0.344729,0.336182],1.0)"
]
},
"execution_count": 1,
"metadata":... | mit |
par2/lamana | docs/package.ipynb | 1 | 6533 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false,
"nbsphinx": "hidden"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Last Run: 2016-07-26 14:03:06\n"
]
}
],
"source": [
"# Hidden Tim... | bsd-3-clause |
ES-DOC/esdoc-jupyterhub | notebooks/csiro-bom/cmip6/models/sandbox-3/ocean.ipynb | 1 | 164419 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"source": [
"# ES-DOC CMIP6 Model Properties - Ocean \n",
"**MIP Era**: CMIP6 \n",
"**Institute**: CSIRO-BOM \n",
"**Source ID**: SANDBOX-3 \n",
"**... | gpl-3.0 |
wkearn/RasterIO.jl | doc/GDALGuide.ipynb | 1 | 108711 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"This Quick Guide is based on the workshop in http://download.osgeo.org/gdal/workshop/foss4ge2015/workshop_gdal.pdf"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# LI... | mit |
SteveDiamond/cvxpy | examples/notebooks/dqcp/concave_fractional_function.ipynb | 3 | 18945 | {
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "concave_fractional_function.ipynb",
"version": "0.3.2",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
}
},
"cells": [
{
... | gpl-3.0 |
quantopian/pyfolio | pyfolio/examples/pyfolio_talk_slides.ipynb | 2 | 2413820 | null | apache-2.0 |
resbazaz/Weekly_Projects | notebooks/2017-08-03-hackyhour.ipynb | 1 | 2403 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Notebook name: \n",
"\n",
"year-month-day-event\n",
"\n",
"(Example: 2017-08-01-hackyhour)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Attendance:\n",
"\n",
"* **Megan... | mit |
Shinichi-Nakagawa/pitchpx-example-ichiro-2016 | analyze_votto_2016.ipynb | 1 | 168978 | {
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Joey Votto analytics(2015-2016)\n",
"\n",
"# ライブラリのインポートと設定\n",
"%matplotlib inline\n",
"\n",
"import pandas as pd\n",
"import seaborn as sns\n... | mit |
ElReySol/UniPrime | PrimeInvestigation00.ipynb | 1 | 20801 | {
"metadata": {
"name": "",
"signature": "sha256:02508117e2889dfd5b1b5c7dc668498b0d0343fc934de63206bb0abe7e78423c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Investigations into the fr... | gpl-2.0 |
coolharsh55/advent-of-code | 2016/python3/Day06.ipynb | 1 | 6781 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Day 6: Signals and Noise\n",
"\n",
"author: Harshvardhan Pandit\n",
"\n",
"license: [MIT](https://opensource.org/licenses/MIT)\n",
"\n",
"[link to problem statement](http://adventofcode.com/2016/day/6)"
]
... | mit |
kevroy314/msl-iposition-pipeline | examples/archived_tests/MSL-Virtual Morris Water Maze TCP Client.ipynb | 1 | 5357 | {
"cells": [
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"'''\n",
" Set up some parsing functions first.\n",
"'''\n",
"\n",
"import io\n",
"import matplotlib.pyplot as plt\n",
"import cv2\n",
"\n",... | gpl-3.0 |
jamessdixon/Kaggle.HomeDepot | ProjectSearchRelevance.Python/Home Depot Product Search Relevance Polynomial.ipynb | 2 | 51472 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Home Depot Product Search Relevance\n",
"The challenge is to predict a relevance score for the provided combinations of search terms and products. To create the ground truth labels, Home Depot has crowdsourced the search/product ... | mit |
masterfish2015/my_project | python/Untitled.ipynb | 3 | 581 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
... | mit |
Jiri-Kremser/bitcoin-insights | graphx-notebook/blockchain.snb.ipynb | 1 | 26761 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "1BD94847B63C4394AF1B3AAFABA54F4C"
},
"source": "# Analyse Blockchain with GraphX"
},
{
"cell_type": "markdown",
"metadata": {
"... | apache-2.0 |
geoneill12/phys202-2015-work | assignments/assignment05/InteractEx01.ipynb | 1 | 5244 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Interact Exercise 01"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Import"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metada... | mit |
fggp/ctcsound | cookbook/07-icsound.ipynb | 1 | 183716 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using ICsound\n",
"\n",
"*csoundmagics* includes an *ICsound* class which is adapted from Andrés Cabrera's [icsound](https://github.com/csound/csound/wiki/icsound) module. *ICsound* is bound to the *%%csound* and *%csound* ma... | lgpl-2.1 |
gamaanderson/2017-AMS-Short-Course-on-Open-Source-Radar-Software | Texture_and_Dealiasing.ipynb | 2 | 775926 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"##Calculating Texture and Dealiasing\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false,
"deletable": true,
"edita... | bsd-2-clause |
jdespraz/deep_generative_networks | autoencoder.ipynb | 1 | 8895 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import numpy as np\n",
"np.random.seed(123)\n",
"\n",
"import os\n",
"from keras.models import Model\n",
"from keras.layers import Input, Convolut... | mit |
xArchange/XGBoost-Analysis | XGB_RF_GB_exp.ipynb | 1 | 53855 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from timeit import default_timer as timer\n",
"\n",
"# Pre-splits the dataset into training and testing folds for all the classifiers to use.\n",
"def sp... | mit |
google/referring-manipulation | third_party/blended_diffusion/CLIP/notebooks/Interacting_with_CLIP.ipynb | 1 | 3599403 | null | apache-2.0 |
JarronL/pynrc | dev_utils/test_speckle_bar.ipynb | 1 | 1277710 | null | mit |
jaidevd/inmantec_fdp | notebooks/day4/05_intro_keras.ipynb | 1 | 5932 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import os\n",
"os.environ['KERAS_BACKEND'] = \"theano\"\n",
"from keras.models import Sequential\n",
"from keras.layers import Dense, Activation\n",
"... | mit |
sevenval/history-diagnostics | benchmark_get_windows.ipynb | 1 | 10837 | {
"metadata": {
"name": "",
"signature": "sha256:9912e70c71d78314e8430de20fc1c9f9ebd6cc5fddc6455abfba2940bc51aafa"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Benchmarking `searchsorted` (1) vs. `uni... | mit |
facaiy/book_notes | 健身/动作整理/硬拉/Untitled.ipynb | 2 | 761 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"硬拉知识整理\n",
"======="
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 动作要领"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 生理学基础"
]
}
],
"metadata": {... | cc0-1.0 |
cgpotts/cs224u | setup.ipynb | 1 | 8424 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Course set-up"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"__author__ = \"Christopher Potts\"\n",
"__version__ = \"CS224u, Stanford, Spring 2022\""
]
... | apache-2.0 |
datactive/bigbang | examples/git-analysis/Git Loading Tutorials.ipynb | 1 | 134780 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Loading From Git\n",
"This will familiarize you with the different ways to access a GitRepo (or MultiGitRepo) object and how to use its data.\n",
"\n",
"\n",
"* Single Repo:\n",
" * remote `get_repo(\"https://g... | mit |
AshivDhondea/SORADSIM | example_notebooks/notebook_001_orbitpropa.ipynb | 1 | 89562 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Demonstrate orbit propagation (OP) using Cowell's method.\n",
"\n",
"Example: ISS TLE file downloaded from celestrak.com on 3 August 2017\n",
"\n",
"ISS (ZARYA)\n",
"\n",
"1 25544U 98067A 17213.83387828 .0000087... | mit |
sjhonatan/intro-python | 08-for/for.ipynb | 2 | 1526 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Playlist no youtube\n",
"\n",
"https://www.youtube.com/playlist?list=PLvu-cXEstYRPZXyug9IqTJq4JtdBxXgeS"
]
},
{
"cell_type": "code",
"execution_count": 51,
"metadata": {
"collapsed": false
},
"output... | mit |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.