repo_name stringlengths 6 92 | path stringlengths 7 220 | copies stringclasses 78
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|---|---|---|---|---|---|
mprat/learningjulia | notebooks/06-image-stitching-part-2.ipynb | 1 | 5758533 | null | mit |
wagnerf42/ws-simulator | result/strategy/simulation_ipynb/simulation.ipynb | 1 | 604524 | {
"cells": [
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"from collections import defaultdict\n",
"from math import log2\n"
]
},
{
"cell_type"... | gpl-3.0 |
chrinide/optunity | notebooks/basic-cross-validation.ipynb | 3 | 27663 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic: cross-validation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This notebook explores the main elements of Optunity's cross-validation facilities, including:\n",
"\n",
"* standard cr... | bsd-3-clause |
ConradScott/IJuliaSamples | GLM/Example 2.2.1 Chronic medical conditions.ipynb | 1 | 475845 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"using DataFrames\n",
"using Distributions\n",
"using Gadfly\n",
"using Optim"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
... | apache-2.0 |
Kaggle/learntools | notebooks/game_ai/raw/tut2.ipynb | 1 | 14794 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"Even if you're new to Connect Four, you've likely developed several game-playing strategies. In this tutorial, you'll learn to use a **heuristic** to share your knowledge with the agent. \n",
"\n... | apache-2.0 |
birdsarah/bokeh-miscellany | old/Clean Teeth.ipynb | 1 | 93987 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
" <div class=\"bk-banner\">\n",
" <a href=\"http://bokeh.pydata.org\" target=\"_blank\" class=\"bk-logo bk-logo-... | gpl-2.0 |
UWPRG/Python | tutorials/Widgets InteractiveGaussian.ipynb | 1 | 43227 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Jim's example of creating an interactive plot to adjust dimensions of a single Gaussian"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
... | mit |
charliememory/AutonomousDriving | CarND-Advanced-Lane-Lines/src/.ipynb_checkpoints/camera_calibration-checkpoint.ipynb | 1 | 875732 | {
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<style> code {background-color : pink !important;} </style>"
],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata"... | gpl-3.0 |
garibaldu/boundary-seekers | Boundary Hunter Ideas/Local Only Boundary Hunter (Hoard).ipynb | 1 | 38757 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"\n",
"import matplotlib\n",
"import autograd.numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import random\n",
"impo... | mit |
biosustain/cameo-notebooks | other/11-multiprocess.ipynb | 1 | 1230 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"In order to take advantage of multicore processors and high performance computing clusters, many functions and methods in cameo where implemented in a parallel mode.\n... | apache-2.0 |
computational-class/cjc2016 | code/tba/powerlaw_fit_intro_Code.ipynb | 5 | 268614 | {
"metadata": {
"name": "",
"signature": "sha256:7d28a26cdf8dcc7dff4df2bbf55b997dd5a7f551e135c7144a649ca564393e89"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import powerlaw\n",
"print(powerlaw._... | mit |
johnpfay/environ859 | 06_WebGIS/Notebooks/GeocodingWithOSM.ipynb | 1 | 8652 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Geocoding using the Open Street Map API\n",
"\n",
"Here we explore an example of using an Application Programming Interface, or API. Briefly, an API is a set of commands we can send over the internet to a remote server, spur... | gpl-3.0 |
mjabri/topographica | doc/Tutorials/som_retinotopy.ipynb | 2 | 31900 | {
"metadata": {
"name": "",
"signature": "sha256:060033d7e8fba6d7cea17c2181c0d5c800db86ae8ee6c3dca89f470937af6c04"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SOM Retinotopy\n",
"\n",
"Th... | bsd-3-clause |
raphaelshirley/regphot | examples/Display.ipynb | 1 | 1739771 | null | mit |
statsmodels/statsmodels.github.io | v0.13.0/examples/notebooks/generated/variance_components.ipynb | 2 | 18614 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Variance Component Analysis\n",
"\n",
"This notebook illustrates variance components analysis for two-level\n",
"nested and crossed designs."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {... | bsd-3-clause |
mne-tools/mne-tools.github.io | 0.14/_downloads/plot_mne_crosstalk_function.ipynb | 1 | 3672 | {
"nbformat_minor": 0,
"nbformat": 4,
"cells": [
{
"execution_count": null,
"cell_type": "code",
"source": [
"%matplotlib inline"
],
"outputs": [],
"metadata": {
"collapsed": false
}
},
{
"source": [
"\n====================... | bsd-3-clause |
mil3na/behavior-study-stackexchange | visualization/table-q3.ipynb | 2 | 17608 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"from __future__ import division\n",
"import pymongo, pandas, random\n",
"import numpy as np\n",
"import matplotlib as mpl\n",
"import matplotlib.pyplot a... | apache-2.0 |
brunez/dl_workshop_upm | notebooks/ann_xor.ipynb | 1 | 4687 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Learning XOR\n",
"This notebook demonstrates a fundamental motivation of representation learning for machine learning: the XOR function.\n",
"\n",
"Learning the XOR function is impossible for a separating-hyperplane based... | unlicense |
neurohackweek/nhw2017 | code/.ipynb_checkpoints/github-users-checkpoint.ipynb | 1 | 1596 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import github\n",
"\n",
"user = \"arokem\"\n",
"password = \"$Cis3SH2$\"\n",
"\n",
"from github import Github\n",
"g = Github(user, password)\n",
... | apache-2.0 |
agrc/Presentations | UGIC/2022/SpatiallyEnabledDataFrames/alpha.ipynb | 2 | 79952 | {
"cells": [
{
"cell_type": "markdown",
"id": "3f8106d9",
"metadata": {
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Ditch the Cursor\n",
"\n",
"\n",
"\n",
"## Editing Feature Classes with Spatialy-Enabled DataFrames"
... | mit |
luwei0917/awsemmd_script | notebook/Optimization/disulfide_analysis.ipynb | 1 | 2159336 | null | mit |
encima/Comp_Thinking_In_Python | Session_7b/7b_Negative_Numbers_Binary Arithmetic.ipynb | 2 | 8374 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"slideshow": {
"slide_type": "slide"
}
},
"source": [
"# Negative Numbers\n",
"\n",
"This works for all positive numbers but how do we show negative numbers?\n",
"\n",
"1001\n",
"\n"
]
}... | mit |
WNoxchi/Kaukasos | FAI_old/Lesson6/Theano_RNN_solved.ipynb | 1 | 17886 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Wayne Nixalo - 1 Jul 2017\n",
"\n",
"RNN Theano\n",
"\n",
"I've been having a problem getting a RNN built in Theano to work. A corpus of\n",
"Nietzsche is the training data. Done correctly, the model should start wi... | mit |
0rC0/DBS-election | iteration2/2_development.ipynb | 2 | 6974 | {
"cells": [
{
"cell_type": "code",
"execution_count": 29,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"#!/usr/bin/python\n",
"\n",
"import psycopg2 as pg2\n",
"import csv\n",
"import re\n",
"from datetime import datetime"
]
},
{
"cell_type": ... | gpl-3.0 |
garth-wells/IA-maths-Jupyter | Overview.ipynb | 2 | 3513 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Supporting notebooks for IA Paper 4 (mathematics) teaching \n",
"\n",
"This collection of Jupyter/Python notebooks is being produced as an experiment in supporting the teaching of mathematical methods to Part IA students at t... | mit |
blink1073/oct2py | example/octavemagic_extension.ipynb | 1 | 994954 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# octavemagic: Octave inside IPython"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Installation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The `octavemagic` ... | mit |
broundy/udacity | nanodegrees/deep_learning_foundations/unit_2/project_2/dlnd_image_classification.ipynb | 2 | 116917 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"source": [
"# Image Classification\n",
"In this project, you'll classify images from the [CIFAR-10 dataset](https://www.cs.toronto.edu/~kriz/cifar.html). The dataset consi... | unlicense |
sods/ods | notebooks/index.ipynb | 1 | 18345 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Notebooks for Open Data Science\n",
"\n",
"[Pandas, Python and Jupyter]($host/Pandas and Python.ipynb) This notebook provides an introduction to the notebook and to the `pandas` library for 'data analysis'.\n",
"* [An int... | bsd-3-clause |
maciejkula/triplet_recommendations_keras | triplet_keras.ipynb | 1 | 10338 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Recommendations in Keras using triplet loss\n",
"Along the lines of BPR [1]. \n",
"\n",
"[1] Rendle, Steffen, et al. \"BPR: Bayesian personalized ranking from implicit feedback.\" Proceedings of the Twenty-Fifth Conferenc... | apache-2.0 |
aoool/vehicle-detection-and-tracking | Vehicle_Detection_and_Tracking.ipynb | 1 | 56728 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Vehicle Detection and Tracking"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"---\n",
"**Author:** Sergey Morozov \n",
"\n",
"---"
]
},
{
"cell_type": "markdown",
"metadata... | mit |
aksp/vrview | examples/orientations/face-tracking-files-to-single-json.ipynb | 1 | 26001 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import subprocess, os, json\n",
"\n",
"VIDEO_ROOT = \"videos/\"\n",
"VIDEO_FRAMES_ROOT = \"video-frames/\"\n",
"\n",
"def get_immediate_subdirectories... | apache-2.0 |
utensil/julia-playground | dl/hello_scikit_learn.ipynb | 1 | 419396 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"I ran the following notebook in a docker container with the following commands:\n",
"\n",
"```\n",
"docker run -it -p 8888:8888 -p 6006:6006 -v `pwd`:/space/ -w /space/ --rm --name md waleedka/modern-deep-learning jupyter n... | mit |
mdeff/ntds_2017 | projects/reports/terrorist_attacks/project/report.ipynb | 1 | 1877374 | null | mit |
utexas-ghosh-group/Experiments | Untitled.ipynb | 1 | 1491 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
"import re\n",
"import os\n",
"import time\n",
"import datetime\n",
"import gc\n",
"#from... | mit |
biof-309-python/BIOF309-2016-Fall | Week_06/Week 06 - 02 - Conditionals.ipynb | 1 | 22885 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Conditions\n",
"\n",
"Source: This material adapted from the [Python for Biologists](http://pythonforbiologists.com/index.php/introduction-to-python-for-biologists/conditions/) website."
]
},
{
"cell_type": "markdow... | mit |
S-Suren/PythonPlayground | Probability_Dice_Analysis.ipynb | 1 | 79855 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": "true"
},
"source": [
"# Table of Contents\n",
" <p><div class=\"lev1 toc-item\"><a href=\"#Analysis-of-dice-rolls.\" data-toc-modified-id=\"Analysis-of-dice-rolls.-1\"><span class=\"toc-item-num\">1 </span>Analysis of di... | gpl-3.0 |
cmgerber/Simple_Gif_Creator | Making_Gifs_from_YouTube.ipynb | 1 | 2826 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Need to intall**\n",
"\n",
"- moviepy (dependent on ffmpeg and ImageMagick - http://zulko.github.io/moviepy/install.ht... | agpl-3.0 |
erikdejonge/github-stars-syncer | Untitled1.ipynb | 1 | 6634 | {
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"[{'archive_url': 'https://api.github.com/repos/pouchdb/pouchdb/{archive_format}{/ref}',\n",
" 'assignees_url': 'https://api.github.c... | gpl-2.0 |
MingChen0919/learning-apache-spark | notebooks/ipynb/RandomForest.ipynb | 1 | 19262 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Pyspark Random Forest Regression"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### 1. Set up spark context and SparkSession"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metad... | mit |
siddhartha-gadgil/ProvingGround | notes/HoTTCoreExperiments.ipynb | 1 | 7287 | {
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"\u001b[32mimport \u001b[39m\u001b[36m$ivy.$ \u001b[39m"
]
},
"execution_count": ... | mit |
necromuralist/machine_learning_studies | machine_learning/udacity/project_1/linear_regression.ipynb | 1 | 5425 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"ein.tags": [
"worksheet-0"
]
},
"source": [
"# Linear Regression"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {
"autoscroll": "json-false",
"collapsed": false,
"ein.tags": [
"worksh... | mit |
jinzekid/codehub | python/Python源代码剖析.ipynb | 1 | 1527 | {
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"object"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = int(10)\n",
"a\n",... | gpl-3.0 |
ericjang/julia-cuda-samples | simplegpu.ipynb | 1 | 7555 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GPU Matrix Addition\n",
"\n",
"This demonstrates the exposed API functions of CUDA.jl (Julia interface for CUDA driver API)\n",
"\n",
"Julia v0.3.11"
]
},
{
"cell_type": "code",
"execution_count": 1,
"... | apache-2.0 |
georgetown-analytics/machine-learning | examples/melissabphd/Reading & Wrangling Diff Dataset for Drug Use Predictor.ipynb | 1 | 17335 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Reading in and wrangling a completely different dataset to use for the Drug Use Predictor\n",
"\n",
"Notebook Author: Melissa Burn\\\n",
"Georgetown University School of Continuing Studies, Certificate in Data Science, ... | mit |
DiCarloLab-Delft/PycQED_py3 | examples/QWG_examples/iPython/3 - SSB vector to big, higher than 1.ipynb | 1 | 3466 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pycqed.instrument_drivers.physical_instruments.QuTech_AWG_Module \\\n",
" import QuTech_AWG_Module\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from... | mit |
tommytwoeyes/continuity | Strategy_Series.ipynb | 2 | 1355 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Strategy for Testing Series Convergence & Divergence\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"data":... | gpl-3.0 |
CalPolyPat/phys202-2015-work | assignments/assignment03/NumpyEx04.ipynb | 1 | 51172 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"# Numpy Exercise 4"
]
},
{
"cell_type": "markdown",
"metadata": {
"nbgrader": {}
},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata"... | mit |
survey-methods/samplics | docs/source/tutorial/replicate_weights.ipynb | 4 | 79912 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Replicate weights\n",
"Replicate weights are usually created for the purpose of variance (uncertainty) estimation. One common use case for replication-based methods is the estimation of non-linear parameters fow which Taylor-bas... | mit |
brumar/WPsolving | make a different version of a problem.ipynb | 1 | 17298 | {
"metadata": {
"name": "",
"signature": "sha256:85b9904b38689dfb012beac7930b9c904651dbdbc769358c02618ee8f6bf501e"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## create a new problem starting with a ol... | mit |
RuiShu/cvae | notebooks/GMM_gradient_checker.ipynb | 1 | 20796 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"require 'nn'"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"mo... | mit |
Danghor/Formal-Languages | Python/Rewrite.ipynb | 1 | 5999 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%HTML\n",
"<style>\n",
".container { width: 100% }\n",
"</style>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Term Simplification via Rewrit... | gpl-2.0 |
performlabrit/ECEM-2017-Eyetracking-in-VR | attic/4. Dispersion.ipynb | 1 | 4733938 | null | gpl-3.0 |
pcmagic/stokes_flow | HelicodsParticles/helicoid_dumb/compare_Darci2020.ipynb | 1 | 4557127 | null | mit |
kl456123/machine_learning | workspace/PCA and SVD.ipynb | 1 | 2961613 | null | mit |
basnijholt/orbitalfield | Phase-diagrams.ipynb | 1 | 10630 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Phase diagram for multiple angles"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Start a `ipcluster` from the Cluster tab in Jupyter or use the command:\n",
"\n",
"```ipcluster start``` \n",... | bsd-2-clause |
jeffalstott/network_clustering_growth | MRC_Notebook/SBM_rewiring_example.ipynb | 1 | 123400 | {
"metadata": {
"name": "",
"signature": "sha256:bdc18ea7e5b7439585474431f509381fbb1295c5d56e18e5d2801972989b2d16"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import networkx as nx\n",
"import com... | mit |
wheeler-microfluidics/mr-box-peripheral-board.py | mr_box_peripheral_board/notebooks/Streaming plot demo.ipynb | 1 | 7435 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": "true"
},
"source": [
"# Table of Contents\n",
" <p><div class=\"lev1 toc-item\"><a href=\"#Embedded-in-Jupyter-notebook\" data-toc-modified-id=\"Embedded-in-Jupyter-notebook-1\"><span class=\"toc-item-num\">1 </span>Embe... | mit |
gileno/curso-citi | notebooks/fizzbuzz.ipynb | 1 | 1807 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Digite num: 10\n"
]
}
],
"source": [
"num = input(\"Digite num: \")\n",
"num = int(num)... | cc0-1.0 |
edjuaro/cuzcatlan | tests/OC_hierarchical_clustering_of_samples.ipynb | 1 | 68929 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-success\">\n",
"Note: if this notebook is taking too long to run, consider using the module HierarchicalClustering v7.3.4 available on gp-beta-ami.genepattern.org"
]
},
{
"cell_type": "markdown",
... | mit |
hmgaudecker/econ-project-templates | {{cookiecutter.project_slug}}/src_python/sandbox/few_agents.ipynb | 4 | 3572 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The ipython notebook is great to interactively play around with some things. Feedback is immediate and directly in front of you.\n",
"\n",
"Once settled on a model / specification, you can just export the code and use it in the... | bsd-3-clause |
ml-ieor/ml-ieor.github.io | notebooks/OutlineOct6th.ipynb | 2 | 3976 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plan for the October 6th, 2016 Columbia Advanced Machine Learning seminar series ###\n",
"\n",
"\n",
"\n",
"** Outline **\n",
"* Generative Adversarial Networks [1]\n",
" * Introduction: Goal, learn (and sampl... | mit |
tbreloff/ExamplePlots.jl | notebooks/scratch/contours.ipynb | 2 | 42821 | {
"cells": [
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAADICAYAAAA0n5+2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAAPYQAAD2EBqD+naQAAIABJREFUeJzsvVmsbVd1LdrGmHOucpenLnxKXHCMQ4IJFx6kINybQI... | mit |
quantumlib/ReCirq | docs/qaoa/binary_paintshop.ipynb | 1 | 21172 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "SzKwuqYESWwm"
},
"source": [
"##### Copyright 2021 The Cirq Developers"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"cellView": "form",
"id": "4yPUsdJxSXFq"
},
"outputs": [],
"source"... | apache-2.0 |
caspar/KentLab | File Selector.ipynb | 1 | 22748 | {
"cells": [
{
"cell_type": "code",
"execution_count": 23,
"metadata": {
"collapsed": false,
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Graham's Computer or Lab: G/L? g\n",
"X coordinate of sample 21\n",
"Y coo... | mit |
parksurk/dl_with_tf | 2-2. logistic regression mnist.ipynb | 1 | 6293 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Logistic Regression with MNIST"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
... | mit |
tensorflow/docs-l10n | site/zh-cn/hub/tutorials/bangla_article_classifier.ipynb | 1 | 23089 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "IDdZSPcLtKx4"
},
"source": [
"##### Copyright 2019 The TensorFlow Hub Authors.\n",
"\n",
"Licensed under the Apache License, Version 2.0 (the \"License\");"
]
},
{
"cell_type": ... | apache-2.0 |
buruzaemon/stats-110 | Lecture_32.ipynb | 1 | 7787 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Lecture 32: Markov chains (cont.), irreducibility, recurrence, transience, reversibility, random walk on an undirected network\n",
"\n",
"\n",
"## Stat 110, Prof. Joe Blitzstein, Harvard University\n",
"\n",
"----... | bsd-3-clause |
ocefpaf/system-test | Theme_1_Baseline/Scenario_1D_Dissolved_Oxygen/Scenario_1D_Dissolved_Oxygen.ipynb | 2 | 129820 | {
"metadata": {
"name": "",
"signature": "sha256:1b6a438c070466f1c3bfe02e2b022472fdb0c88e4a2853b8ead87f68043f5cff"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"from utilities import * \n",
"css_sty... | unlicense |
jairideout/scikit-bio-cookbook | Index.ipynb | 1 | 1541 | {
"metadata": {
"name": "",
"signature": "sha256:a6ca15047262c22bbb790e374ab37a15aefba952493abe27d54f96ed773bcf13"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"The [scikit-bio](http://sc... | bsd-3-clause |
cleuton/datascience | nlp/sentimentAnalysis/SentimentTweets.ipynb | 2 | 3370 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sentimentos de tweets quando ao lançamento do Falcon Heavy"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"library(twitteR)\n",
"library(ROAuth)\n",
... | apache-2.0 |
lkmsk/OpenDataToNeo4J | DB_OpenData/Explore_With_py2neo_NetworkX.ipynb | 1 | 197574 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Explore DB-OpenData with py2neo and NetworkX\n",
"\n",
"In this notebook we explore the carsharing data from Deutsche Bahn, they're migrated to a Neo4J graph database. For this we use two libraries: [py2neo](http://py2neo.org... | apache-2.0 |
usantamaria/iwi131 | ipynb/23-ProcesamientoDeTexto/Texto.ipynb | 2 | 36101 | {
"cells": [
{
"cell_type": "code",
"execution_count": 19,
"metadata": {
"collapsed": false,
"slideshow": {
"slide_type": "skip"
}
},
"outputs": [
{
"data": {
"text/html": [
"<style>\n",
"\n",
".reveal {\n",
"overflow: visible;\n",
"}\... | cc0-1.0 |
kdheepak/psst | docs/notebooks/interactive_visuals/Demo.ipynb | 1 | 34560 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# How `NetworkModel` Works"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"\n",... | mit |
JAmarel/LiquidCrystals | ElectroOptics/MinimizeAttempt.ipynb | 1 | 39845 | {
"cells": [
{
"cell_type": "code",
"execution_count": 18,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from scipy.integrate import quad, dblquad\n",
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"from scipy.optimize i... | mit |
bgruening/EDeN | examples/Sequence_example.ipynb | 2 | 25214 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#Example\n",
"\n",
"Consider sequences that are increasingly different. EDeN allows to turn them into vectors, whose similarity is decreasing."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
... | gpl-3.0 |
enakai00/jupyter_ml4se_commentary | Solutions/Titanic Example.ipynb | 1 | 234907 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. データを取り込んで特徴を確認"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
... | apache-2.0 |
pcmagic/stokes_flow | head_Force/do_calculate_table_loc/jump_branch-Copy4.ipynb | 2 | 9528178 | null | mit |
jdhaines/ProcessTechnology | test.ipynb | 2 | 1234 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Testy Test Test Notebook\n",
"Header markdown above..."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": false
},
"outputs": [
{
"name": "stdout",
"output_type":... | mit |
JoyMonteiro/CliMT | lib/examples/CliMT -- Tropical Cyclone.ipynb | 1 | 4553 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# DCMIP test case 4 demo - 3\n",
"-------\n",
"\n",
"Here, the ability of the dynamical core to simulate a tropical cyclone is tested.\n",
"\n",
"The main aim here is to simulate a tropical cyclone using a simple ph... | bsd-3-clause |
RinaldoB/course-neuro-datasim | Day3_2_solutions.ipynb | 1 | 119891 | {
"metadata": {
"name": ""
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"#bigger plots\n",
"figsize(14,5)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_numb... | mit |
DawesLab/LabNotebooks | Dashboard widgets.ipynb | 1 | 17240 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"extensions": {
"jupyter_dashboards": {
"version": 1,
"views": {
"grid_default": {
"hidden": true
},
"report_default": {}
}
}
}
},
"outputs": [],
"source": [
... | mit |
Naereen/notebooks | agreg/public2018_D1.ipynb | 1 | 56114 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"toc": "true"
},
"source": [
"# Table of Contents\n",
" <p><div class=\"lev1 toc-item\"><a href=\"#Texte-d'oral-de-modélisation---Agrégation-Option-Informatique\" data-toc-modified-id=\"Texte-d'oral-de-modélisation---Agrégation-Option-Info... | mit |
barjacks/pythonrecherche | Kursteilnehmer/Sven Millischer/06 /01 Rückblick For-Loop-Übungen.ipynb | 2 | 8689 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 10 For-Loop-Rückblick-Übungen"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In den Teilen der folgenden Übungen habe ich den Code mit \"XXX\" ausgewechselt. Es gilt in allen Übungen, den korrekten ... | mit |
NelisW/ComputationalRadiometry | 03-Introduction-to-Radiometry.ipynb | 1 | 699014 | {
"cells": [
{
"cell_type": "raw",
"metadata": {},
"source": [
"%This notebook demonstrates the use of the workpackage template, replace with your own.\n",
"\n",
"\\documentclass[english]{workpackage}[1996/06/02]\n",
"\n",
"% input the common preamble content (required by the ipnb2latex ... | mpl-2.0 |
getsmarter/bda | module_2/M2_NB1_SourcesOfData.ipynb | 1 | 22557 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div align=\"right\">Python 3.6 Jupyter Notebook</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Sources of data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
... | mit |
PMEAL/OpenPNM | examples/reference/data_management/data_exchange_between_objects.ipynb | 1 | 2432 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Data Exchange Between Objects\n",
"\n",
"In OpenPNM there are different types of objects for storing different types of data, however, it is possible to access data from one object via another object. \n",
"\n",
"Th... | mit |
ESMG/ESMG-configs | ocean_only/Rossby_soliton/open/Untitled.ipynb | 1 | 14250 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"%pylab inline"
]
},
{
... | gpl-3.0 |
sysid/nbs | cnn/tw_vgg16.ipynb | 1 | 435247 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Using Convolutional Neural Networks"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is running on theano!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Welcom... | mit |
bbglab/adventofcode | 2016/ferran/day6.ipynb | 1 | 2950 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"collapsed": false
},
"source": [
"# Chellenge 6\n",
"\n",
"## Challenge 6.1"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"myinput = '... | mit |
jdvelasq/machine-learning | regresion-R-medical-expenses.ipynb | 1 | 5836 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Predicción de gastos médicos\n",
"===\n",
"\n",
"**Juan David Velásquez Henao** \n",
"jdvelasq@unal.edu.co \n",
"Universidad Nacional de Colombia, Sede Medellín \n",
"Facultad de Minas \n",
"Medellín, C... | mit |
eabdullin/nlp_mthesis | yandex_translate.ipynb | 1 | 496 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"url_base = 'https://translate.yandex.net/api/v1.5/tr.json/translate'\n",
"api_key = \"trnsl.1.1.20160131T134129Z.551138fac8444143.d4d1893d45993c98d622f57494c87a805719cf86\""
]
},
{
... | mit |
katyhuff/berkeley | possible_topics/learn_and_teach.ipynb | 5 | 28652 | {
"cells": [
{
"cell_type": "code",
"execution_count": 79,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# need pandas\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 80,
"metadata": {
"collapsed": false
},
"outputs": ... | bsd-3-clause |
ekostat/ekostat_calculator | .ipynb_checkpoints/sharkdata_dwca_obis_env_data_test-checkpoint.ipynb | 1 | 3246 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Reload when code changed:\n",
"%load_ext autoreload\n",
"%autoreload 2\n",
"%pwd"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs"... | mit |
Diyago/Machine-Learning-scripts | general studies/learning python/learning pandas/ДЗ №3.ipynb | 1 | 26081 | {
"cells": [
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"years=range(1880, 2017)\n",
"\n",
"pieces = []\n",
... | apache-2.0 |
luwei0917/awsemmd_script | notebook/Optimization/family_fold_may11.ipynb | 1 | 3928674 | null | mit |
deepmind/dm_control | tutorial.ipynb | 1 | 68918 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "MpkYHwCqk7W-"
},
"source": [
"# **`dm_control` tutorial**\n",
"\n",
"[](https://colab.research.google.com/github/deepmind/d... | apache-2.0 |
jmhsi/justin_tinker | data_science/courses/learning_dl_packages/tensorflow_tutorials.ipynb | 1 | 1306 | {
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%run classify_image.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"displ... | apache-2.0 |
google/physics-math-tutorials | colabs/RiemannHypothesisColab.ipynb | 1 | 36796 | {"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"RiemannHypothesisColab.ipynb","provenance":[{"file_id":"1hRgC0_nh-J-f7iRpkTiMQDl7PaBwb9gV","timestamp":1636424929084},{"file_id":"1kXeIG7pikUVbh0gFbOhV3O58lA-nt0pz","timestamp":1540052134550},{"file_id":"1eQlldmZxxsazCTwZ9JKppGpgQb4wGLMd","timestamp":1539093... | apache-2.0 |
huongttlan/statsmodels | examples/notebooks/statespace_sarimax_internet.ipynb | 10 | 9122 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# SARIMAX: Model selection, missing data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The example mirrors Durbin and Koopman (2012), Chapter 8.4 in application of Box-Jenkins methodology to fit ARMA... | bsd-3-clause |
thalesians/tsa | src/jupyter/python/distrs.ipynb | 1 | 118003 | {
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import os, sys\n",
"sys.path.append(os.path.abspath(... | apache-2.0 |
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