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repo_name stringlengths 6 77	path stringlengths 8 215	license stringclasses 15 values	cells list	types list
ES-DOC/esdoc-jupyterhub	notebooks/uhh/cmip6/models/sandbox-2/ocnbgchem.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Ocnbgchem\nMIP Era: CMIP6\nInstitute: UHH\nSource ID: SANDBOX-2\nTopic: Ocnbgchem\nSub-Topics: Tracers. \nProperties: 65 (37 required)\nModel descriptions: Model description details\nInitialized From: -- \nNotebook Help: Goto notebook help page\nNotebook Initialised: 2018-02-15 16:...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
ingmarschuster/rkhs_demo	RKHS_in_Machine_learning.ipynb	gpl-3.0	[ "$\\newcommand{\\Reals}{\\mathbb{R}}\n\\newcommand{\\Nats}{\\mathbb{N}}\n\\newcommand{\\PDK}{\\mathbf{k}}\n\\newcommand{\\IS}{\\mathcal{X}} \n\\newcommand{\\FM}{\\Phi} \n\\newcommand{\\Gram}{K} \n\\newcommand{\\RKHS}{\\mathcal{H}}\n\\newcommand{\\prodDot}[2]{\\left\\langle#1,#2\\right\\rangle}\n\\DeclareMathOperato...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
charlesll/Examples	PySolExExample.ipynb	gpl-2.0	[ "Charles Le Losq\nFriday, 22 May 2015\nModified the 16 June 2015.\nGeophysical Laboratory,\nCarnegie Institution for Science\nExample of use of pysolex, the library using the software SolEx developped by Fred Witham, University of Bristol.\nFrom the header of the solex.cpp:\n\"An object that calculates the solubili...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
sunilmallya/dl-twitch-series	E3_finetuning_randall_not_randall.ipynb	apache-2.0	[ "Build a model to detect if Randall is in the image or not!\nRandall or Not\nDataset\n\nRandall : s3://ranman-selfies\nNot Randall: http://vis-www.cs.umass.edu/lfw/lfw.tgz (celeb faces)\n\nmost of the code is borrowed from\nhttps://github.com/dmlc/mxnet-notebooks/blob/master/python/tutorials/finetune-CNN-catsvsdog...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
idekerlab/graph-services	notebooks/DEMO.ipynb	mit	[ "cxMate Service DEMO\nBy Ayato Shimada, Mitsuhiro Eto\nThis DEMO shows\n1. detect communities using an igraph's community detection algorithm\n2. paint communities (nodes and edges) in different colors\n3. perform layout using graph-tool's sfdp algorithm", "# Tested on:\n!python --version", "Send CX to service ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
indranilsinharoy/PyZDDE	Examples/IPNotebooks/01 Notes on ipzCaptureWindow functions.ipynb	mit	[ "Using ipzCaptureWindow and ipzCaptureWindow2 for embedding graphic analysis windows into notebook\n<img src=\"https://raw.githubusercontent.com/indranilsinharoy/PyZDDE/master/Doc/Images/articleBanner_01_ipzcapturewindow.png\" height=\"230\">\nPlease feel free to e-mail any corrections, comments and suggestions to ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
google/jax-md	notebooks/customizing_potentials_cookbook.ipynb	apache-2.0	[ "<a href=\"https://colab.research.google.com/github/google/jax-md/blob/main/notebooks/customizing_potentials_cookbook.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\nCustomizing Potentials in JAX MD\nThis cookbook was contributed by Carl ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
PythonFreeCourse/Notebooks	week07/1_Classes.ipynb	mit	[ "<img src=\"images/logo.jpg\" style=\"display: block; margin-left: auto; margin-right: auto;\" alt=\"לוגו של מיזם לימוד הפייתון. נחש מצויר בצבעי צהוב וכחול, הנע בין האותיות של שם הקורס: לומדים פייתון. הסלוגן המופיע מעל לשם הקורס הוא מיזם חינמי ללימוד תכנות בעברית.\">\n<span style=\"text-align: right; direction: rtl...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
jamesmarva/maths-with-python	04-basic-plotting.ipynb	mit	[ "Plotting\nThere are many Python plotting libraries depending on your purpose. However, the standard general-purpose library is matplotlib. This is often used through its pyplot interface.", "from matplotlib import pyplot\n%matplotlib inline", "The command %matplotlib inline is not a Python command, but an IPyt...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ellisonbg/talk-2015	12-JupyterLab.ipynb	mit	[ "Building Blocks for Interactive Computing\nWhat are the building blocks for interactive computing?", "%load_ext load_style\n%load_style images.css\nfrom IPython.display import display, Image", "File browser", "Image('images/lego-filebrowser.png', width='80%')", "Terminal", "Image('images/lego-terminal.pn...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
tensorflow/docs-l10n	site/ko/tutorials/estimator/premade.ipynb	apache-2.0	[ "Copyright 2019 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
carthach/essentia	src/examples/tutorial/example_clickdetector.ipynb	agpl-3.0	[ "ClickDetector use example\nThis algorithm detects the locations of impulsive noises (clicks and pops) on\nthe input audio frame. It relies on LPC coefficients to inverse-filter the\naudio in order to attenuate the stationary part and enhance the prediction\nerror (or excitation noise)[1]. After this, a matched fil...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
GoogleCloudPlatform/asl-ml-immersion	notebooks/kubeflow_pipelines/pipelines/solutions/kfp_pipeline_vertex_automl_batch_predictions.ipynb	apache-2.0	[ "Continuous Training with AutoML Vertex Pipelines with Batch Predictions\nLearning Objectives:\n1. Learn how to use Vertex AutoML pre-built components\n1. Learn how to build a Vertex AutoML pipeline with these components using BigQuery as a data source\n1. Learn how to compile, upload, and run the Vertex AutoML pip...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
msschwartz21/craniumPy	experiments/templates/TEMP-landmarks.ipynb	gpl-3.0	[ "Introduction: Landmarks", "import deltascope as ds\nimport deltascope.alignment as ut\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom sklearn.preprocessing import normalize\nfrom scipy.optimize import minimize\n\nimport os\nimport tqdm\nimport json\nimport time", "Import raw...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mne-tools/mne-tools.github.io	dev/_downloads/1242d47b65d952f9f80cf19fb9e5d76e/35_eeg_no_mri.ipynb	bsd-3-clause	[ "%matplotlib inline", "EEG forward operator with a template MRI\nThis tutorial explains how to compute the forward operator from EEG data\nusing the standard template MRI subject fsaverage.\n.. caution:: Source reconstruction without an individual T1 MRI from the\n subject will be less accurate. Do no...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
Centre-Alt-Rendiment-Esportiu/att	notebooks/Serial Ports.ipynb	gpl-3.0	[ "<h1>Serial Ports</h1>\n<hr style=\"border: 1px solid #000;\">\n<span>\n<h2>Serial Port abstraction for ATT.</h2>\n</span>\n<br>\n<span>\nThis notebook shows the ATT Serial Port abstraction module.<br>\nThis module was created for enabling testing on ATT framework.\nThe Serial Port abstraction provides an Abstract ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mdda/fossasia-2016_deep-learning	notebooks/2-CNN/5-TransferLearning/5-ImageClassifier-keras.ipynb	mit	[ "Re-Purposing a Pretrained Network\nSince a large CNN is very time-consuming to train (even on a GPU), and requires huge amounts of data, is there any way to use a pre-calculated one instead of retraining the whole thing from scratch?\nThis notebook shows how this can be done. And it works surprisingly well.\nHow ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
molgor/spystats	notebooks/.ipynb_checkpoints/model_by_chunks-checkpoint.ipynb	bsd-2-clause	[ "Here I'm process by chunks the entire region.", "# Load Biospytial modules and etc.\n%matplotlib inline\nimport sys\nsys.path.append('/apps')\nimport django\ndjango.setup()\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n## Use the ggplot style\nplt.style.use('ggplot')\n\nfrom external...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
steinam/teacher	jup_notebooks/datenbanken/Sommer_2015.ipynb	mit	[ "Subselects", "%load_ext sql\n\n\n%sql mysql://steinam:steinam@localhost/sommer_2015", "Sommer 2015\nDatenmodell\n\nAufgabe\nErstellen Sie eine Abfrage, mit der Sie die Daten aller Kunden, die Anzahl deren Aufträge, die Anzahl der Fahrten und die Summe der Streckenkilometer erhalten. Die Ausgabe soll nach Kunde...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ES-DOC/esdoc-jupyterhub	notebooks/cams/cmip6/models/sandbox-3/ocnbgchem.ipynb	gpl-3.0	[ "ES-DOC CMIP6 Model Properties - Ocnbgchem\nMIP Era: CMIP6\nInstitute: CAMS\nSource ID: SANDBOX-3\nTopic: Ocnbgchem\nSub-Topics: Tracers. \nProperties: 65 (37 required)\nModel descriptions: Model description details\nInitialized From: -- \nNotebook Help: Goto notebook help page\nNotebook Initialised: 2018-02-15 16...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
mvaz/osqf2015	notebooks/DataPreparation.ipynb	mit	[ "Introduction\nSimply the first step to prepare the data for the following notebooks", "import Quandl\nimport pandas as pd\nimport numpy as np\nimport blaze as bz", "Data source is http://www.quandl.com.\nWe use blaze to store data.", "with open('../.quandl_api_key.txt', 'r') as f:\n api_key = f.read()\n\n...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
GoogleCloudPlatform/vertex-ai-samples	notebooks/community/migration/UJ14 legacy AutoML Vision Video Classification.ipynb	apache-2.0	[ "# Copyright 2021 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
ryan-leung/PHYS4650_Python_Tutorial	notebooks/05-Python-Functions-Class.ipynb	bsd-3-clause	[ "Python Functions and Classes\nSometimes you need to define your own functions to work with custom data or solve some problems. A function can be defined with a prefix def. A class is like an umbrella that can contains many data types and functions, it is defined by class prefix.\n<a href=\"https://colab.research.g...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
gprakhar/janCC	Janacare_Habits_dataset_upto-7May2016.ipynb	bsd-3-clause	[ "Hello World!\nThis notebook describes the effort filter out users to resurrect with Digital Marketing\nClean up data\nde-duplicate : based on email i'd\nPartitioning the Data:\ntwo methods - \nA) cluster the data and see how many clusters are there: used MeanShift method\nB) Bin the data based on age_on_platform\...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
tensorflow/docs-l10n	site/en-snapshot/addons/tutorials/optimizers_lazyadam.ipynb	apache-2.0	[ "Copyright 2020 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
peterwittek/qml-rg	Archiv_Session_Spring_2017/Exercises/05_APS Captcha.ipynb	gpl-3.0	[ "import keras\nimport itertools as it\nimport matplotlib.pyplot as pl\nfrom tempfile import TemporaryDirectory\n\nTMPDIR = TemporaryDirectory()\nkeras.backend.set_image_data_format('channels_first')", "Preprocessing", "import os \nfrom skimage import io\nfrom skimage.color import rgb2gray\nfrom skimage import t...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
robotcator/gensim	gensim Quick Start.ipynb	lgpl-2.1	[ "# Getting Started with gensim\nThe goal of this tutorial is to get a new user up-and-running with gensim. This notebook covers the following objectives.\n## Objectives\n\nInstalling gensim.\nAccessing the gensim Jupyter notebook tutorials.\nPresenting the core concepts behind the library.\n\nInstalling gensim\nBe...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
guilgautier/DPPy	notebooks/fast_sampling_of_beta_ensembles.ipynb	mit	[ "Companion notebook of the paper Fast sampling of $\\beta$-ensembles\nby Guillaume Gautier, Rémi Bardenet, and Michal Valko\nSee also the arXiv preprint: 2003.02344 \n<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Companion-notebook-of-the-p...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
daphnei/nn_chatbot	homeworks/XOR/HW1_report.ipynb	mit	[ "Homework 2\nDaphne Ippolito", "import xor_network", "What issues did you have?\nThe first issue that I has was that I was trying to output a single scalar whose value could be thresholded to determine whether the network should return TRUE or FALSE. It turns out loss functions for this are much more complicate...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
astro4dev/OAD-Data-Science-Toolkit	Teaching Materials/Programming/Python/Python3Espanol/1_Introduccion/03. Numeros y jerarquía de operaciones.ipynb	gpl-3.0	[ "Números y jerarquía de operaciones\n\nNúmeros enteros y flotantes\nJerarquía de operaciones\nAsignación de variables\n\nNúmeros enteros y flotantes\nCon los números se pueden realizar los siguientes tipos de operaciones:\n\| Operación \| Resultado \|\n\| --------- \| --------------- \|\n\| + \| Suma ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
maxalbert/tohu	notebooks/v4/Primitive_generators.ipynb	mit	[ "Primitive generators\nThis notebook contains tests for tohu's primitive generators.", "import tohu\nfrom tohu.v4.primitive_generators import \nfrom tohu.v4.dispatch_generators import \nfrom tohu.v4.utils import print_generated_sequence\n\nprint(f'Tohu version: {tohu.__version__}')", "Constant\nConstant simpl...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
fsilva/deputado-histogramado	notebooks/Deputado-Histogramado-3.ipynb	gpl-3.0	[ "Deputado Histogramado\nexpressao.xyz/deputado/\nComo processar as sessões do parlamento Português\nÍndice\n\nReunír o dataset\nContando as palavras mais comuns\nFazendo histogramas\nRepresentações geograficas\nSimplificar o dataset e exportar para o expressa.xyz/deputado/\n\nO que se passou nas mais de 4000 sessõe...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
TESScience/FPE_Test_Procedures	Evaluating Parameter Interdependence.ipynb	mit	[ "Evaluating Parameter Interdependence\nTest run on 10/29/15 by Ed Bokhour.\nUsing SD PCB Interface Board serial number 002, SD PCB Driver Board serial number 002, and SD PCB Video Board serial number 001. Running with new wrapper (FPE_Wrapper_6.1.2, for San Diego PCBs, dated 10/19/15.\nSet up the FPE\nRemember tha...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mne-tools/mne-tools.github.io	stable/_downloads/82d9c13e00105df6fd0ebed67b862464/ssp_projs_sensitivity_map.ipynb	bsd-3-clause	[ "%matplotlib inline", "Sensitivity map of SSP projections\nThis example shows the sources that have a forward field\nsimilar to the first SSP vector correcting for ECG.", "# Author: Alexandre Gramfort <alexandre.gramfort@inria.fr>\n#\n# License: BSD-3-Clause\n\nimport matplotlib.pyplot as plt\n\nfrom mne import...	[ "code", "markdown", "code", "markdown", "code" ]
sourabhrohilla/ds-masterclass-hands-on	session-2/python/Topic_Model_Recommender.ipynb	mit	[ "Topic Based Recommender\nTopic Based Recommender\n\nRepresent articles in terms of Topic Vector\nRepresent user in terms of Topic Vector of read articles\nCalculate cosine similarity between read and unread articles \nGet the recommended articles \n\nDescribing parameters:\n1. PATH_ARTICLE_TOPIC_DISTRIBUTION: spec...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
DSSG2017/florence	dev/notebooks/Distributions_MM.ipynb	mit	[ "Plotting distributions\nFirst, import relevant libraries:", "import warnings\nwarnings.filterwarnings('ignore')\n\nimport numpy as np\nimport pandas as pd\n%matplotlib inline\nimport matplotlib.pyplot as plt", "Then, load the data (takes a few moments):", "# Load data\nuda = pd.read_csv(\"./aws-data/user_dis...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
jgarciab/wwd2017	class4/class4_timeseries.ipynb	gpl-3.0	[ "Working with data 2017. Class 4\nContact\nJavier Garcia-Bernardo\ngarcia@uva.nl\n0. Structure\n\nStats\nDefinitions\nWhat's a p-value?\nOne-tailed test vs two-tailed test\nCount vs expected count (binomial test)\nIndependence between factors: ($\\chi^2$ test) \n\n\nIn-class exercises to melt, pivot, concat, m...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
MartyWeissman/Python-for-number-theory	P3wNT Notebook 3.ipynb	gpl-3.0	[ "Part 3: Lists and the sieve of Eratosthenes in Python 3.x\nPython provides a powerful set of tools to create and manipulate lists of data. In this part, we take a deep dive into the Python list type. We use Python lists to implement and optimize the Sieve of Eratosthenes, which will produce a list of all prime ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
kit-cel/wt	SC468/LDPC_Optimization_AWGN.ipynb	gpl-2.0	[ "Optimization of Degree Distributions on the AWGN\nThis code is provided as supplementary material of the lecture Channel Coding 2 - Advanced Methods.\nThis code illustrates\n* Using linear programming to optimize degree distributions on the AWGN channel using EXIT charts", "import numpy as np\nimport matplotlib....	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
UWSEDS/LectureNotes	PreFall2018/02-Python-and-Data/Lecture-Python-and-Data.ipynb	bsd-2-clause	[ "# Some styling stuff... ignore this for now!\nfrom IPython.display import HTML\nHTML(\"\"\"<style>\n .rendered_html {font-size: 140%;}\n .rendered_html h1, h2 {text-align:center;}\n</style>\"\"\")", "Software Engineering for Data Scientists\nManipulating Data with Python\nCSE 599 B1\nToday's Objectives\n1. Ope...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
Upward-Spiral-Science/grelliam	code/classification_simulation.ipynb	apache-2.0	[ "Simulated Classifcation\n\nState assumptions\nFormally define classification/regression problem\nprovide algorithm for solving problem (including choosing hyperparameters as appropriate)\nsample data from a simulation setting inspired by your data (from both null and alternative as defined before)\ncompute accurac...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
machinelearningnanodegree/stanford-cs231	solutions/levin/assignment2/FullyConnectedNets.ipynb	mit	[ "Fully-Connected Neural Nets\nIn the previous homework you implemented a fully-connected two-layer neural network on CIFAR-10. The implementation was simple but not very modular since the loss and gradient were computed in a single monolithic function. This is manageable for a simple two-layer network, but would be...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
ktmud/deep-learning	gan_mnist/Intro_to_GANs_Exercises.ipynb	mit	[ "Generative Adversarial Network\nIn this notebook, we'll be building a generative adversarial network (GAN) trained on the MNIST dataset. From this, we'll be able to generate new handwritten digits!\nGANs were first reported on in 2014 from Ian Goodfellow and others in Yoshua Bengio's lab. Since then, GANs have exp...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
Hugovdberg/timml	notebooks/timml_notebook1_sol.ipynb	mit	[ "TimML Notebook 1\nA well in uniform flow\nConsider a well in the middle aquifer of a three aquifer system. Aquifer properties are given in Table 1. The well is located at $(x,y)=(0,0)$, the discharge is $Q=10,000$ m$^3$/d and the radius is 0.2 m. There is a uniform flow from West to East with a gradient of 0.002. ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
dsevilla/bdge	mongo/sesion4.ipynb	mit	[ "NoSQL (MongoDB) (sesión 4)\n\nEsta hoja muestra cómo acceder a bases de datos MongoDB y también a conectar la salida con Jupyter. Se puede utilizar el shell propio de MongoDB en la máquina virtual usando el programa mongo. La diferencia es que ese programa espera código Javascript y aquí trabajaremos con Python.",...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
GoogleCloudPlatform/vertex-ai-samples	notebooks/community/sdk/sdk_automl_image_object_detection_online_export_edge.ipynb	apache-2.0	[ "# Copyright 2021 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
AtmaMani/pyChakras	python_crash_course/python_cheat_sheet_2.ipynb	mit	[ "Python cheat sheet - iterations\nTable of contents\n - Functions\n - Classes\n - Exception handling\nFunctions\nSpecify optional parameters in the end. Specify the default values for optional parameters with = value notation\ndef func_name(arg1, arg2=None):\n operations\n return value", "def func_add_numbe...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
paoloRais/lightfm	examples/quickstart/quickstart.ipynb	apache-2.0	[ "Quickstart\nIn this example, we'll build an implicit feedback recommender using the Movielens 100k dataset (http://grouplens.org/datasets/movielens/100k/).\nThe code behind this example is available as a Jupyter notebook\nLightFM includes functions for getting and processing this dataset, so obtaining it is quite ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
jo-tez/aima-python	csp.ipynb	mit	[ "CONSTRAINT SATISFACTION PROBLEMS\nThis IPy notebook acts as supporting material for topics covered in Chapter 6 Constraint Satisfaction Problems of the book Artificial Intelligence: A Modern Approach. We make use of the implementations in csp.py module. Even though this notebook includes a brief summary of the mai...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
AstroHackWeek/AstroHackWeek2016	notebook-tutorial/notebooks/01-Tips-and-tricks.ipynb	mit	[ "Best practices\nLet's start with pep8 (https://www.python.org/dev/peps/pep-0008/)\n\nImports should be grouped in the following order:\n\nstandard library imports\nrelated third party imports\nlocal application/library specific imports\n\nYou should put a blank line between each group of imports.\nPut any relevant...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
BrownDwarf/ApJdataFrames	notebooks/Hernandez2014.ipynb	mit	[ "ApJdataFrames Hernandez2014\nTitle: A SPECTROSCOPIC CENSUS IN YOUNG STELLAR REGIONS: THE σ ORIONIS CLUSTER\nAuthors: Jesus Hernandez, Nuria Calvet, Alice Perez, Cesar Briceno, Lorenzo Olguin, Maria E Contreras, Lee Hartmann, Lori E Allen, Catherine Espaillat, and Ramírez Hernan \nData is from this paper:\nhttp://...	[ "markdown", "code", "markdown", "code", "markdown" ]
GoogleCloudPlatform/vertex-ai-samples	notebooks/community/sdk/sdk_automl_image_object_detection_online.ipynb	apache-2.0	[ "# Copyright 2021 Google LLC\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed ...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
musketeer191/job_analytics	.ipynb_checkpoints/jobtitle_skill-checkpoint.ipynb	gpl-3.0	[ "Building JobTitle-Skill matrix\nRunning LDA on document-skill matrix, where each document is a job post, still does not give good results!!! What is the problem here?\nIt seems that the job post level has too many noises:\n+ other info not relating to skills i.e. salary, location, working time, required experience...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
halexand/NB_Distribution	.ipynb_checkpoints/KL rambling notes on Python-checkpoint.ipynb	mit	[ "Use this to keep track of useful code bits as I learn Python\nKrista, August 19, 2015\nShortcut Action\nShift-Enter run cell\nCtrl-Enter run cell in-place\nAlt-Enter run cell, insert below\n\nCtrl / (Ctrl and then the slash)...will comment out any selected text within a block of code", "#First up...lis...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
endgameinc/youarespecial	BSidesLV -- your model isn't that special -- (1) MLP.ipynb	mit	[ "Preliminaries\nWe're going to build and compare a few malware machine learning models in this series of Jupyter notebooks. Some of them require a GPU. I've used a Titan X GPU for this exercise. If yours isn't as beefy, you may get tensorflow memory errors that may require modifying some of the code, namely file...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
mmaelicke/felis_python1	felis_python1/lectures/06_Classes.ipynb	mit	[ "Classes\nOne of the main features in the Python programming language is its object oriented structure. Thus, beside procedual programming (scripting) it's also possible to use Python for object oriented Programming (OOP). \nIn a nutshell, everything in Python is an object and can be understood as an instance of a ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
arcyfelix/Courses	17-09-17-Python-for-Financial-Analysis-and-Algorithmic-Trading/10-Quantopian-Platform/02-Basic-Algorithm-Methods.ipynb	apache-2.0	[ "Basic Algorithm Methods\nLet's algorithmically test our earlier optimized tech portfolio strategy with Quantopian!\nTHIS CODE ONLY WORKS ON QUANTOPIAN. EACH CELL CORRESPONDS WITH A PART OF THE VIDEO LECTURE. MAKE SURE TO WATCH THE VIDEOS FOR CLARITY ON THIS!\ninitialize()\ninitialize() is called exactly once when ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ivannz/study_notes	year_15_16/fall_2015/game theoretic foundations of ml/labs/SVM-lab.ipynb	mit	[ "Применение машины опорных векторов к выявлению фальшивых купюр\nПодключим необходимые библиотеки.", "import numpy as np, pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn import *\n%matplotlib inline\n\nrandom_state = np.random.RandomState( None )\n\ndef collect_result( grid_, names = [ ] ) :\n df =...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
xpharry/Udacity-DLFoudation	your-first-network/.ipynb_checkpoints/dlnd-your-first-neural-network-checkpoint.ipynb	mit	[ "Your first neural network\nIn this project, you'll build your first neural network and use it to predict daily bike rental ridership. We've provided some of the code, but left the implementation of the neural network up to you (for the most part). After you've submitted this project, feel free to explore the data ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ktmud/deep-learning	student-admissions/StudentAdmissions.ipynb	mit	[ "Predicting Student Admissions with Neural Networks\nIn this notebook, we predict student admissions to graduate school at UCLA based on three pieces of data:\n- GRE Scores (Test)\n- GPA Scores (Grades)\n- Class rank (1-4)\nThe dataset originally came from here: http://www.ats.ucla.edu/\nLoading the data\nTo load t...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
kit-cel/wt	nt1/vorlesung/extra/dsss.ipynb	gpl-2.0	[ "Content and Objectives\n\nShow spreading in time and frequency domain\nBPSk symbols are being pulse-shaped by rectangular w. and wo. spreading\n\nImport", "# importing\nimport numpy as np\n\nimport matplotlib.pyplot as plt\nimport matplotlib\n\n# showing figures inline\n%matplotlib inline\n\n# plotting options \...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
Jay-Jay-D/LeanSTP	Jupyter/KitchenSinkQuantBookTemplate.ipynb	apache-2.0	[ "Welcome to The QuantConnect Research Page\nRefer to this page for documentation https://www.quantconnect.com/docs#Introduction-to-Jupyter\nContribute to this template file https://github.com/QuantConnect/Lean/blob/master/Jupyter/BasicQuantBookTemplate.ipynb\nQuantBook Basics\nStart QuantBook\n\nAdd the references ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
johnbachman/emcee	docs/_static/notebooks/quickstart.ipynb	mit	[ "%matplotlib inline\n\n%config InlineBackend.figure_format = \"retina\"\n\nfrom matplotlib import rcParams\nrcParams[\"savefig.dpi\"] = 100\nrcParams[\"figure.dpi\"] = 100\nrcParams[\"font.size\"] = 20", "Quickstart\nThe easiest way to get started with using emcee is to use it for a project. To get you started, h...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
turi-code/tutorials	dss-2016/churn_prediction/churn-tutorial.ipynb	apache-2.0	[ "Forecasting customer churn\nChurn prediction is the task of identifying users that are likely to stop using a service, product or website. In this notebook, you will learn how to:\nTrain & consume a model to forecast user churn\n\nDefine the boundary at which churn happens.\nDefine a churn period.\nTrain a model u...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mne-tools/mne-tools.github.io	0.15/_downloads/plot_info.ipynb	bsd-3-clause	[ "%matplotlib inline", "The :class:Info <mne.Info> data structure\nThe :class:Info <mne.Info> data object is typically created\nwhen data is imported into MNE-Python and contains details such as:\n\ndate, subject information, and other recording details\nthe sampling rate\ninformation about the data ch...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
computational-class/cjc2016	code/04.PythonCrawler_selenium.ipynb	mit	[ "数据抓取\n\n使用Selenium操纵浏览器\n\n\n\n王成军 \nwangchengjun@nju.edu.cn\n计算传播网 http://computational-communication.com\nSelenium 是一套完整的web应用程序测试系统，包含了\n- 测试的录制（selenium IDE）\n- 编写及运行（Selenium Remote Control）\n- 测试的并行处理（Selenium Grid）。\nSelenium的核心Selenium Core基于JsUnit，完全由JavaScript编写，因此可以用于任何支持JavaScript的浏览器上。selenium可以模拟真实浏览...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
pm4py/pm4py-core	notebooks/2_event_data_filtering.ipynb	gpl-3.0	[ "Event Data Filtering\nby: Sebastiaan J. van Zelst\nLike any data-driven field, the successful application of process mining needs data munging and crunching.\nIn pm4py, you can munge and crunch your data in two ways, i.e., you can write lambda functions and apply them on\nyour event log, or, you can apply pre-buil...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
jpallas/beakerx	doc/python/ChartingAPI.ipynb	apache-2.0	[ "Python API to BeakerX Interactive Plotting\nYou can access Beaker's native interactive plotting library from Python.\nPlot with simple properties\nPython plots has syntax very similar to Groovy plots. Property names are the same.", "from beakerx import *\nimport pandas as pd\n\ntableRows = pd.read_csv('../resour...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
LimeeZ/phys292-2015-work	assignments/assignment11/OptimizationEx01.ipynb	mit	[ "Optimization Exercise 1\nImports", "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.optimize as opt", "Hat potential\nThe following potential is often used in Physics and other fields to describe symmetry breaking and is often known as the \"hat potential\":\n$$ V(x) = -a ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ecervera/mindstorms-nb	task/quadrat.ipynb	mit	[ "Exercici: fer un quadrat\n<img src=\"img/bart-simpson-chalkboard.jpg\" align=\"right\" width=250>\nA partir de les instruccions dels moviments bàsics, heu de fer un programa per a que el robot avance i gire 90 graus, de manera de faça una trajectòria quadrada.\nL'estratègia és simple: repetiu quatre vegades el cod...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
JamesSample/icpw	correct_toc_elev.ipynb	mit	[ "%matplotlib inline\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport imp\nfrom sqlalchemy import create_engine", "TOC and elevation corrections\nSome further changes to the ICPW trends analysis are required:\n\n\nHeleen has discovered some strange results for TOC for some of the Canadian sites (see e...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
turbomanage/training-data-analyst	courses/machine_learning/deepdive/05_artandscience/labs/c_neuralnetwork.ipynb	apache-2.0	[ "Neural Network\nLearning Objectives:\n * Use the DNNRegressor class in TensorFlow to predict median housing price\nThe data is based on 1990 census data from California. This data is at the city block level, so these features reflect the total number of rooms in that block, or the total number of people who live ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
nproctor/phys202-2015-work	assignments/assignment06/DisplayEx01.ipynb	mit	[ "Display Exercise 1\nImports\nPut any needed imports needed to display rich output the following cell:", "from IPython.display import Image\nfrom IPython.display import HTML\n\nassert True # leave this to grade the import statements", "Basic rich display\nFind a Physics related image on the internet and display...	[ "markdown", "code", "markdown", "code", "markdown", "code" ]
wy1iu/sphereface	tools/caffe-sphereface/examples/02-fine-tuning.ipynb	mit	[ "Fine-tuning a Pretrained Network for Style Recognition\nIn this example, we'll explore a common approach that is particularly useful in real-world applications: take a pre-trained Caffe network and fine-tune the parameters on your custom data.\nThe advantage of this approach is that, since pre-trained networks are...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
XinyiGong/pymks	notebooks/filter.ipynb	mit	[ "Filter Example\nThis example demonstrates the connection between MKS and signal\nprocessing for a 1D filter. It shows that the filter is in fact the\nsame as the influence coefficients and, thus, applying the predict\nmethod provided by the MKSLocalizationnModel is in essence just applying a filter.", "%matplotl...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
turbomanage/training-data-analyst	courses/machine_learning/deepdive2/structured/labs/3c_bqml_dnn_babyweight.ipynb	apache-2.0	[ "LAB 3c: BigQuery ML Model Deep Neural Network.\nLearning Objectives\n\nCreate and evaluate DNN model with BigQuery ML\nCreate and evaluate DNN model with feature engineering with ML.TRANSFORM.\nCalculate predictions with BigQuery's ML.PREDICT\n\nIntroduction\nIn this notebook, we will create multiple deep neural ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
kubeflow/kfp-tekton-backend	samples/core/ai_platform/ai_platform.ipynb	apache-2.0	[ "Chicago Crime Prediction Pipeline\nAn example notebook that demonstrates how to:\n* Download data from BigQuery\n* Create a Kubeflow pipeline\n* Include Google Cloud AI Platform components to train and deploy the model in the pipeline\n* Submit a job for execution\n* Query the final deployed model\nThe model forec...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
phoebe-project/phoebe2-docs	development/tutorials/beaming_boosting.ipynb	gpl-3.0	[ "Beaming and Boosting\nDue to concerns about accuracy, support for Beaming & Boosting has been disabled as of the 2.2 release of PHOEBE (although we hope to bring it back in a future release).\nIt may come as surprise that support for Doppler boosting has been dropped in PHOEBE 2.2. This document details the underl...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
srippa/nn_deep	NN playground.ipynb	mit	[ "resourcus\n\nI am trask blog - simple introduction to NN\nNeural bnetwork tutorial - walk all the way. A similar tutorial\nAndrew Ng ML course\nPedro domingos course\nNN papers\nBrief introduction to deep learning. Based on the Deep learning lab\n\nCourses\n\nCSC321 Winter 2015: Introduction to NN - Toronto\n\nPap...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
statkraft/shyft-doc	notebooks/repository/repositories-intro.ipynb	lgpl-3.0	[ "Exposing the API\nIntroduction\nAt its core, Shyft provides functionality through an API (Application Programming Interface). All the functionality of Shyft is available through this API.\nWe begin the tutorials by introducing the API as it provides the building blocks for the framework. Once you have a good under...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
IST256/learn-python	content/lessons/04-Iterations/LAB-Iterations.ipynb	mit	[ "Class Coding Lab: Iterations\nThe goals of this lab are to help you to understand:\n\nHow loops work.\nThe difference between definite and indefinite loops, and when to use each.\nHow to build an indefinite loop with complex exit conditions.\nHow to create a program from a complex idea.\n\nUnderstanding Iterations...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
minesh1291/Practicing-Kaggle	zillow2017/H2Opy_v0.ipynb	gpl-3.0	[ "Table of Contents\n<p><div class=\"lev1 toc-item\"><a href=\"#import-Packages\" data-toc-modified-id=\"import-Packages-1\"><span class=\"toc-item-num\">1  </span>import Packages</a></div><div class=\"lev2 toc-item\"><a href=\"#H2O-init\" data-toc-modified-id=\"H2O-init-11\"><span class=\"toc-item-num\">1...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
tensorflow/docs-l10n	site/zh-cn/guide/keras/custom_callback.ipynb	apache-2.0	[ "Copyright 2020 The TensorFlow Authors.", "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# https://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable ...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
TomTranter/OpenPNM	examples/tutorials/Intro to OpenPNM - Advanced.ipynb	mit	[ "Tutorial 3 of 3: Advanced Topics and Usage\nLearning Outcomes\n\nUse different methods to add boundary pores to a network\nManipulate network topology by adding and removing pores and throats\nExplore the ModelsDict design, including copying models between objects, and changing model parameters\nWrite a custom por...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
quantopian/research_public	notebooks/lectures/Case_Study_Comparing_ETFs/answers/notebook.ipynb	apache-2.0	[ "Exercises: Comparing ETFs - Answer Key\nBy Christopher van Hoecke, Maxwell Margenot, and Delaney Mackenzie\nLecture Link :\nhttps://www.quantopian.com/lectures/statistical-moments\nhttps://www.quantopian.com/lectures/hypothesis-testing\nIMPORTANT NOTE:\nThis lecture corresponds to the statistical moments and hypot...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
Danghor/Algorithms	Python/Chapter-09/Dijkstra.ipynb	gpl-2.0	[ "from IPython.core.display import HTML\nwith open('../style.css') as file:\n css = file.read()\nHTML(css)", "Dijkstra's Shortest Path Algorithm\nThe notebook Set.ipynb implements <em style=\"color:blue\">sets</em> as\n<a href=\"https://en.wikipedia.org/wiki/AVL_tree\">AVL trees</a>.\nThe API provided by Set of...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "cod...
drabastomek/learningPySpark	Chapter06/LearningPySpark_Chapter06.ipynb	gpl-3.0	[ "Introducing ML package of PySpark\nPredict chances of infant survival with ML\nLoad the data\nFirst, we load the data.", "import pyspark.sql.types as typ\n\nlabels = [\n ('INFANT_ALIVE_AT_REPORT', typ.IntegerType()),\n ('BIRTH_PLACE', typ.StringType()),\n ('MOTHER_AGE_YEARS', typ.IntegerType()),\n ('...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
chengsoonong/mclass-sky	mclearn/knfst/python/test.ipynb	bsd-3-clause	[ "import numpy as np\nimport scipy as sp\nimport pandas as pd\nimport urllib.request\nimport os\nimport shutil\nimport tarfile\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets, cross_validation, metrics\nfrom sklearn.preprocessing import KernelCenterer\n\n%matplotlib notebook", "First we need to down...	[ "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
kimkipyo/dss_git_kkp	통계, 머신러닝 복습/160620월_17일차_나이브 베이즈 Naive Bayes/2.실전 예제.ipynb	mit	[ "베르누이의 경우 실습 예제", "X = np.array([[1,0,0],[1,1,1], [0,1,1],[0,1,0],[0,0,1],[1,1,1]])\ny0 = np.zeros(2)\ny1 = np.ones(4)\ny = np.hstack([y0, y1])\nprint(X)\nprint(y)\n\nfrom sklearn.naive_bayes import BernoulliNB\nclf_bern = BernoulliNB().fit(X, y)\n\nclf_bern.classes_\n\nclf_bern.class_count_\n\nfc = clf_bern.feat...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
adamsteer/nci-notebooks	.ipynb_checkpoints/Point cloud to HDF-checkpoint.ipynb	apache-2.0	[ "What is the proposed task:\n\ningest some liDAR points into a HDF file\ningest the aircraft trajectory into the file\nanything else\n\n...and then extract data from the HDF file at different rates using a spatial 'query'\nWhat do the data look like?\nASCII point clouds with the following attributes, currently used...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
trsherborne/learn-python	lesson4.ipynb	mit	[ "LSESU Applicable Maths Python Lesson 4\n15/11/16\nToday we will be learning about\n* Data Structures - Official documentation on Data Structures here\n * Lists\n * Tuples\n * Dictionaries\n* Introduction to the Pandas library\n Recap from Week 3\n\nStrings\n\n```\nday = input('Enter the day of the month y...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
ramhiser/Keras-Tutorials	notebooks/06_autoencoder.ipynb	mit	[ "Autoencoders\nI've been exploring how useful autoencoders are and how painfully simple they are to implement in Keras. In this post, my goal is to better understand them myself, so I borrow heavily from the Keras blog on the same topic. So rather than sprinkling references to the Keras blog throughout the post, ju...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
the-deep-learners/study-group	neural-networks-and-deep-learning/src/run_network.ipynb	mit	[ "Network from Nielsen's Chapter 1\nhttp://neuralnetworksanddeeplearning.com/chap1.html#implementing_our_network_to_classify_digits\nLoad MNIST Data", "import mnist_loader\n\ntraining_data, validation_data, test_data = mnist_loader.load_data_wrapper()", "Set up Network", "import network\n\n# 784 (28 x 28 pixel...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
statsmodels/statsmodels.github.io	v0.13.1/examples/notebooks/generated/pca_fertility_factors.ipynb	bsd-3-clause	[ "statsmodels Principal Component Analysis\nKey ideas: Principal component analysis, world bank data, fertility\nIn this notebook, we use principal components analysis (PCA) to analyze the time series of fertility rates in 192 countries, using data obtained from the World Bank. The main goal is to understand how th...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
mdeff/ntds_2017	projects/reports/face_manifold/NTDS_Project.ipynb	mit	[ "Manifold Learning on Face Data\nAtul Kumar Sinha, Karttikeya Mangalam and Prakhar Srivastava\nIn this project, we explore manifold learning on face data to embed high dimensional face images into a lower dimensional embedding. We hypothesize that euclidean distance in this lower dimensional embedding reflects imag...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
dietmarw/EK5312_ElectricalMachines	Chapman/Ch5-Problem_5-10.ipynb	unlicense	[ "Excercises Electric Machinery Fundamentals\nChapter 5\nProblem 5-10", "%pylab notebook", "Description\nA synchronous machine has a synchronous reactance of $1.0\\,\\Omega$ per phase and an armature resistance of $0.1\\,\\Omega$ per phase. \n\nIf $\\vec{E}A = 460\\,V\\angle-10°$ and $\\vec{V}\\phi = 480\\,V\\an...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
ramabrahma/data-sci-int-capstone	.ipynb_checkpoints/data-exploration-life-insurance-checkpoint.ipynb	gpl-3.0	[ "Exploration of Prudential Life Insurance Data\nData retrieved from:\nhttps://www.kaggle.com/c/prudential-life-insurance-assessment\nFile descriptions:\n\ntrain.csv - the training set, contains the Response values\ntest.csv - the test set, you must predict the Response variable for all rows in this file\nsample_sub...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code" ]
g-weatherill/notebooks	gmpe-smtk/Ground Motion IMs Short.ipynb	agpl-3.0	[ "Calculating Ground Motion Intensity Measures\nThe SMTK contains two modules for the characterisation of ground motion:\n1) smtk.response_spectrum\nThis module contains methods for calculation of the response of a set of single degree-of-freedom (SDOF) oscillators using an input time series. Two methods are current...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...
scraperwiki/databaker	databaker/tutorial/Finding_your_way.ipynb	agpl-3.0	[ "Opening and previewing\nThis uses the tiny excel spreadsheet example1.xls. It is small enough to preview inline in this notebook. But for bigger spreadsheet tables you will want to open them up in a separate window.", "\n# Load in the functions\nfrom databaker.framework import *\n\n# Load the spreadsheet\ntabs...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown" ]
NazBen/impact-of-dependence	notebooks/grid-search.ipynb	mit	[ "Conservative Estimation using a Grid Seach Minimization\nThis notebook illustrates the different steps for a conservative estimation using a grid search minimization.\nClassic Libraries", "import openturns as ot\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\n%matplotlib inline\n%l...	[ "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "markdown", "code", "mar...

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