Machine-Learning-And-Statistics-Repos

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Machine-Learning-And-Statistics-Repos

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Useful list of Machine Learning And Statistics Repositories

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# List of Machine Learning And Statistics Repositories
Useful list of Machine Learning And Statistics Repositories

[2012-paper-diginorm](https://github.com/wavelets/2012-paper-diginorm) - Paper source, analysis notebook, and data generation/analysis scripts for diginorm paper

[2014-talks](https://github.com/wavelets/2014-talks) - This is the official repository for slides and talks from GopherCon 2014

[538model](https://github.com/wavelets/538model) - 538 Election Forecasting Model

[9m](https://github.com/wavelets/9m) - 9m Unicode URL Shortener

[aa228-notebook](https://github.com/wavelets/aa228-notebook) - IJulia notebooks for AA228/CS238 Decision Making Under Uncertainty course at Stanford University

[aerosolve](https://github.com/wavelets/aerosolve) - A machine learning package built for humans.

[airbnb](https://github.com/wavelets/airbnb) - Data collection for Airbnb business

[AL](https://github.com/wavelets/AL) - Active Learning

[alpha](https://github.com/wavelets/alpha) - Open-source web microblogging client for App.net

[AlwaysRemember](https://github.com/wavelets/AlwaysRemember) - Zipfian capstone project - Dan Morris

[amazonaccess](https://github.com/wavelets/amazonaccess) - Amazon Employee Access Challenge

[angular-nvd3](https://github.com/wavelets/angular-nvd3) - An AngularJS directive for NVD3 reusable charting library (based on D3). Easily customize your charts via JSON API.

[annoy](https://github.com/wavelets/annoy) - Approximate Nearest Neighbors in C++/Python optimized for memory usage and loading/saving to disk

[ark-tweet-nlp](https://github.com/wavelets/ark-tweet-nlp) - CMU ARK Twitter Part-of-Speech Tagger

[audio_fingerprinting](https://github.com/wavelets/audio_fingerprinting) - Exploration of using image processing algorithms in other domains

[awesome-artificial-intelligence](https://github.com/wavelets/awesome-artificial-intelligence) - A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers

[awesome-d3](https://github.com/wavelets/awesome-d3) - A list of D3 libraries, plugins and utilities

[awesome-go](https://github.com/wavelets/awesome-go) - A curated list of awesome Go frameworks, libraries and software. Inspired by awesome-python.

[awesome-python](https://github.com/wavelets/awesome-python) - A curated list of awesome Python frameworks, libraries and software. Inspired by awesome-php.

[babynames](https://github.com/wavelets/babynames) - Fun with the Social Security Administration’s baby name data

[backbone.baseview](https://github.com/wavelets/backbone.baseview) - A simple base view class for Backbone.View,

[backbone.radio](https://github.com/wavelets/backbone.radio) - Messaging patterns for Backbone applications.

[backgrid](https://github.com/wavelets/backgrid) - Finally, an easily stylable semantic HTML data grid widget with a Javascript API that doesn’t suck.

[bank](https://github.com/wavelets/bank) - Statsd and Metricsd frontend for UDP packets aggregation

[barnes-hut-sne](https://github.com/wavelets/barnes-hut-sne) - Unofficial repository for the Barnes-Hut version of t-SNE by Laurens van der Maaten

[bashplotlib](https://github.com/wavelets/bashplotlib) - plotting in the terminal

[Bastien-Theano-Workshop](https://github.com/wavelets/Bastien-Theano-Workshop) - Fred’s Theano Workshop

[BayesDataAnalysisWithPyMC](https://github.com/wavelets/BayesDataAnalysisWithPyMC) - Python (PyMC) adaptation of the R code from “Doing Bayesian Data Analysis”

[bayesian](https://github.com/wavelets/bayesian) - Utility for Bayesian reasoning

[Bayesian-data-analysis-with-PyMC2](https://github.com/wavelets/Bayesian-data-analysis-with-PyMC2) - Bayesian data analysis with PyMC(2)

[BayesPy](https://github.com/wavelets/BayesPy) - Bayesian Inference Tools in Python

[bci-challenge-ner-2015](https://github.com/wavelets/bci-challenge-ner-2015) - Code and documentation for the winning solution at the BCI Challenge @ NER 2015 : https://www.kaggle.com/c/inria-bci-challenge

[benchm-ml](https://github.com/wavelets/benchm-ml) -

[biaxial-rnn-music-composition](https://github.com/wavelets/biaxial-rnn-music-composition) - A recurrent neural network designed to generate classical music.

[bird](https://github.com/wavelets/bird) - Pure Python implementation of the BIRD algorithm for (structured)-sparsity based denoising of multichannel array

[bitcoin](https://github.com/wavelets/bitcoin) - Bitcoin Core integration/staging tree

[blaze-scipy-2014](https://github.com/wavelets/blaze-scipy-2014) - Slides for scipy 2014 conference

[blocks](https://github.com/wavelets/blocks) - A Theano framework for building and training neural networks

[bokeh](https://github.com/wavelets/bokeh) - Interactive Web Plotting for Python

[bolt](https://github.com/wavelets/bolt) - Bolt Online Learning Toolbox

[book](https://github.com/wavelets/book) - Crypto 101, the introductory book on cryptography.

[bootstrap-wysiwyg](https://github.com/wavelets/bootstrap-wysiwyg) - Tiny bootstrap-compatible WISWYG rich text editor

[brain](https://github.com/wavelets/brain) - Neural networks in JavaScript

[brainmets](https://github.com/wavelets/brainmets) - Survival prediction for brain metastases

[breeze](https://github.com/wavelets/breeze) - Breeze is a library for numerical processing, machine learning, and natural language processing. Its primary focus is on being generic, clean, and powerful without sacrificing (much) efficiency. Breeze is the merger of the ScalaNLP and Scalala projects, because one of the original maintainers is unable to continue development. The Scalala parts are largely rewritten.

[cached-property](https://github.com/wavelets/cached-property) - A decorator for caching properties in classes.

[cayley](https://github.com/wavelets/cayley) - An open-source graph database

[ccv](https://github.com/wavelets/ccv) - C-based/Cached/Core Computer Vision Library, A Modern Computer Vision Library

[censored_regression](https://github.com/wavelets/censored_regression) - Linear regression with lower-bound labels

[chainer](https://github.com/wavelets/chainer) - A flexible framework of neural networks for deep learning

[chicago-atlas](https://github.com/wavelets/chicago-atlas) - View citywide information about health trends and take action near you to improve your own health.

[citibike_analysis](https://github.com/wavelets/citibike_analysis) - Let’s analyze Citibike data!

[cloudy-tweets](https://github.com/wavelets/cloudy-tweets) - Machine Learning solution for Kaggle.com’s “Partly Sunny with a Chance of Hashtags”

[CNN_sentence](https://github.com/wavelets/CNN_sentence) - CNNs for sentence classification

[Coelho2015_NetsDetermination](https://github.com/wavelets/Coelho2015_NetsDetermination) -

[coins](https://github.com/wavelets/coins) - Bitcoin value tracker

[compneuro](https://github.com/wavelets/compneuro) - Computational Neuroscience class materials in PyDSTool based on Hugh Wilson’s book (and some from Eugene Izhikevich’s book). DRAFT VERSION! See files_info.txt for index. Please improve and discuss!

[Conjecture](https://github.com/wavelets/Conjecture) - Scalable Machine Learning in Scalding

[convnetjs](https://github.com/wavelets/convnetjs) - Deep Learning in Javascript. Train Convolutional Neural Networks (or ordinary ones) in your browser.

[cookbook-code](https://github.com/wavelets/cookbook-code) - Recipes of the IPython Cookbook, the definitive guide to high-performance scientific computing and data science in Python

[copper](https://github.com/wavelets/copper) - Fast, easy and intuitive machine learning prototyping.

[CoverTree](https://github.com/wavelets/CoverTree) - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree

[crab](https://github.com/wavelets/crab) - Crab - A recommendation engine library for Python

[craigslist-checker](https://github.com/wavelets/craigslist-checker) - Send text when a new Craigslist posting matches a given keyword or phrase

[crime](https://github.com/wavelets/crime) - None

[cs229-project](https://github.com/wavelets/cs229-project) - Recommender System Project for CS229 at Stanford in Fall 2012

[cudamat](https://github.com/wavelets/cudamat) - Python module for performing basic dense linear algebra computations on the GPU using CUDA.

[cudnn-python-wrappers](https://github.com/wavelets/cudnn-python-wrappers) - Python wrappers for the NVIDIA cuDNN libraries

[curfil](https://github.com/wavelets/curfil) - CUDA Random Forest implementation for Image Labeling tasks

[cython_lstm](https://github.com/wavelets/cython_lstm) - Python LSTM Python library for getting things done quickly, greatly, and without waiting 50 years for compilation

[d3.chart](https://github.com/wavelets/d3.chart) - A framework for creating reusable charts with d3.js.

[d3js-presentation](https://github.com/wavelets/d3js-presentation) -

[daft](https://github.com/wavelets/daft) - Render some probabilistic graphical models using matplotlib

[darkmarket](https://github.com/wavelets/darkmarket) - the world’s fastest response to a govt takedown of digital markets

[dash](https://github.com/wavelets/dash) - Flask, JS, and CSS boilerplate for interactive, web-based visualization apps in Python

[data](https://github.com/wavelets/data) - Data and code behind the stories and interactives at FiveThirtyEight

[Data-Analysis-and-Machine-Learning-Projects](https://github.com/wavelets/Data-Analysis-and-Machine-Learning-Projects) - Repository of teaching materials, code, and data for my data analysis and machine learning projects.

[dataanalysis](https://github.com/wavelets/dataanalysis) - Coursera data analysis course, done in Python

[DataGotham2013](https://github.com/wavelets/DataGotham2013) -

[datascience-anthology-pydata](https://github.com/wavelets/datascience-anthology-pydata) - PyData, The Complete Works of

[datascientist_tutorial](https://github.com/wavelets/datascientist_tutorial) -

[datasets](https://github.com/wavelets/datasets) - Datasets of some standard computer vision / deep learning benchmarks

[data_hacking](https://github.com/wavelets/data_hacking) - Click Security Data Hacking Project

[data_hacks](https://github.com/wavelets/data_hacks) - Command line utilities for data analysis

[db-readings](https://github.com/wavelets/db-readings) - Readings in Databases

[decision-weights](https://github.com/wavelets/decision-weights) - Homegrown analysis of Prospect Theory: Math, turkers and python =)

[deep-limits](https://github.com/wavelets/deep-limits) - Repo for a paper about constructing priors on very deep models.

[deep-pink](https://github.com/wavelets/deep-pink) -

[DeepANN](https://github.com/wavelets/DeepANN) - Theano based deep ANN learning code

[DeepLearning](https://github.com/wavelets/DeepLearning) - Deep Learning (Python, C/C++, Java, Scala)

[DeepLearningTutorials](https://github.com/wavelets/DeepLearningTutorials) - Tutorials from deeplearning.net converted for Torch

[deepmat](https://github.com/wavelets/deepmat) - Matlab Code for Restricted/Deep Boltzmann Machines and Autoencoders

[deepnet](https://github.com/wavelets/deepnet) - Implementation of some deep learning algorithms.

[democracy-measurement-model](https://github.com/wavelets/democracy-measurement-model) - Replication materials for Bayesian measurement error model of dichotomous measures of democracy.

[Diffusion-Probabilistic-Models](https://github.com/wavelets/Diffusion-Probabilistic-Models) - Reference implementation for Deep Unsupervised Learning using Nonequilibrium Thermodynamics

[diffusion-segmentation](https://github.com/wavelets/diffusion-segmentation) - A collection of image segmentation algorithms based on diffusion methods

[dimensionality-reduction-for-sparse-binary-data](https://github.com/wavelets/dimensionality-reduction-for-sparse-binary-data) - convert a lot of zeros and ones to fewer real numbers

[dist_lda](https://github.com/wavelets/dist_lda) - distributed latent dirichlet allocation

[dl-machine](https://github.com/wavelets/dl-machine) - Scripts to setup a GPU / CUDA-enabled compute server with libraries for deep learning

[docopt](https://github.com/wavelets/docopt) - Pythonic command line arguments parser, that will make you smile

[Doing_bayesian_data_analysis](https://github.com/wavelets/Doing_bayesian_data_analysis) - Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke

[DonorsChoose_Visualization](https://github.com/wavelets/DonorsChoose_Visualization) -

[downhill](https://github.com/wavelets/downhill) - Stochastic gradient optimization routines for Theano

[dpmm](https://github.com/wavelets/dpmm) - Dirichlet process mixture model.

[dsutils](https://github.com/wavelets/dsutils) - Utilities for Python’s data science ecosystem

[DWURecyclingAlert](https://github.com/wavelets/DWURecyclingAlert) - A drop-in code snippet that dynamically detects non-recycled UI elements inside your UITableViewCells.

[Elements-of-Statistical-Learning](https://github.com/wavelets/Elements-of-Statistical-Learning) - Contains LaTeX, SciPy and R code providing solutions to exercises in Elements of Statistical Learning (Hastie, Tibshirani & Friedman)

[elevator.js](https://github.com/wavelets/elevator.js) - Finally, a “back to top” button that behaves like a real elevator.

[emcee](https://github.com/wavelets/emcee) - The Python ensemble sampling toolkit for affine-invariant MCMC

[energy](https://github.com/wavelets/energy) - Artsy Folio, The Partner iPhone / iPad app.

[epoch](https://github.com/wavelets/epoch) - A general purpose, real-time visualization library.

[equity](https://github.com/wavelets/equity) - Series Seed Preferred Stock

[euroscipy2012_document](https://github.com/wavelets/euroscipy2012_document) -

[exploratory_computing_with_python](https://github.com/wavelets/exploratory_computing_with_python) -

[fabric-ec2](https://github.com/wavelets/fabric-ec2) - Some helpers to simplify running Fabric tasks on EC2 instances

[faker](https://github.com/wavelets/faker) - Faker is a Python package that generates fake data for you.

[fastnet](https://github.com/wavelets/fastnet) -

[fb-mac-messenger](https://github.com/wavelets/fb-mac-messenger) - ⚡️ Mac app wrapping Facebook’s Messenger for desktop

[featureforge](https://github.com/wavelets/featureforge) - A set of tools for creating and testing machine learning features, with a scikit-learn compatible API

[fix-macosx](https://github.com/wavelets/fix-macosx) -

[flow](https://github.com/wavelets/flow) - Volumetric Particle Flow - http://david.li/flow

[FM_FTRL](https://github.com/wavelets/FM_FTRL) - Hashed Factorization Machine with Follow The Regularized Leader for Kaggle Avazu Click-Through Rate Competition

[freebayes](https://github.com/wavelets/freebayes) - Bayesian haplotype-based polymorphism discovery and genotyping.

[fuel](https://github.com/wavelets/fuel) - A data pipeline framework for machine learning

[fuzzywuzzy](https://github.com/wavelets/fuzzywuzzy) - Fuzzy String Matching in Python

[gdbn](https://github.com/wavelets/gdbn) - George Dahl’s gdbn: Pre-trained deep neural networks

[gender-data-pkg](https://github.com/wavelets/gender-data-pkg) - A data package for R containing historical data sets about gender

[gensim](https://github.com/wavelets/gensim) - Topic Modelling for Humans

[getting-started-with-haskell](https://github.com/wavelets/getting-started-with-haskell) - notes on where to find Haskell tutorials and tips to complete them

[ggplot-tutorial](https://github.com/wavelets/ggplot-tutorial) -

[Ghost](https://github.com/wavelets/Ghost) - Just a blogging platform

[githut](https://github.com/wavelets/githut) - Visualization of data from github archive.

[glove-python](https://github.com/wavelets/glove-python) - Toy Python implementation of http://www-nlp.stanford.edu/projects/glove/

[go](https://github.com/wavelets/go) - The Open Source Data Science Masters

[gobot](https://github.com/wavelets/gobot) - A Go framework/set of libraries for robotics and physical computing

[goji](https://github.com/wavelets/goji) - Goji is a minimalistic web framework for Golang inspired by Sinatra that’s high in antioxidants

[golearn](https://github.com/wavelets/golearn) - Machine Learning for Golang

[goop](https://github.com/wavelets/goop) - A dependency manager for Go (golang), inspired by Bundler.

[Grasp-and-lift-EEG-challenge](https://github.com/wavelets/Grasp-and-lift-EEG-challenge) - Code and documentation for the winning sollution at the Grasp-and-Lift EEG Detection challenge

[GroundHog](https://github.com/wavelets/GroundHog) - Library for implementing RNNs with Theano

[group_lasso](https://github.com/wavelets/group_lasso) -

[gtor](https://github.com/wavelets/gtor) - A General Theory of Reactivity

[h2o](https://github.com/wavelets/h2o) - h2o = fast statistical, machine learning & math runtime for bigdata

[hawkes](https://github.com/wavelets/hawkes) -

[hebel](https://github.com/wavelets/hebel) - GPU-Accelerated Deep Learning Library in Python

[hedgehog](https://github.com/wavelets/hedgehog) - Re-implementation of method in Playing Atari with Deep Reinforcement Learning paper.

[higgsml](https://github.com/wavelets/higgsml) - The winning solution to the The Higgs Boson Machine Learning Challenge.

[hillary-clinton-emails](https://github.com/wavelets/hillary-clinton-emails) - Code to transform Hillary’s emails from raw PDF documents to a SQLite database

[hiscore](https://github.com/wavelets/hiscore) - HiScore makes creating sophisticated scores easy

[histogram.go](https://github.com/wavelets/histogram.go) -

[hmmlearn](https://github.com/wavelets/hmmlearn) - Hidden Markov Models in Python, with scikit-learn like API

[hockey](https://github.com/wavelets/hockey) - Hockey analytics

[How-to-Make-a-Computer-Operating-System](https://github.com/wavelets/How-to-Make-a-Computer-Operating-System) - How to Make a Computer Operating System in C++

[huginn](https://github.com/wavelets/huginn) - Build agents that monitor and act on your behalf. Your agents are standing by!

[hydra](https://github.com/wavelets/hydra) - Multi-process MongoDB collection copier.

[hyperopt-sklearn](https://github.com/wavelets/hyperopt-sklearn) - Hyper-parameter optimization for sklearn

[HypTrails](https://github.com/wavelets/HypTrails) -

[ida](https://github.com/wavelets/ida) - An introduction to data analysis, using R. Experimental.

[iir](https://github.com/wavelets/iir) - Machine Learning / Natural Language Processing / Information Retrieval

[impyla](https://github.com/wavelets/impyla) - Python client to Cloudera Impala

[inception](https://github.com/wavelets/inception) - None

[intercooler-js](https://github.com/wavelets/intercooler-js) - A declarative, REST-ful data binding library for web applications

[intro-python-scarpentry-paris-2013](https://github.com/wavelets/intro-python-scarpentry-paris-2013) - Teaching material on Python for the Software Carpentry bootcamp at Telecom ParisTech, Paris, 2013

[intro2stats](https://github.com/wavelets/intro2stats) - Introduction to Statistics using Python

[ipython](https://github.com/wavelets/ipython) - Official repository for IPython itself. Other repos in the IPython organization contain things like the website, documentation builds, etc.

[ipython-notebooks](https://github.com/wavelets/ipython-notebooks) - Collection of IPython Notebooks

[IPython-plotly](https://github.com/wavelets/IPython-plotly) - A collection of IPython notebooks that feature Plotly graphs. Content moved to https://plotl.ly/ipython-notebooks/

[iTorch](https://github.com/wavelets/iTorch) - IPython kernel for Torch with visualization and plotting

[ivalice](https://github.com/wavelets/ivalice) - Boosting and ensemble learning in Python.

[iwae](https://github.com/wavelets/iwae) - Code to train Importance Weighted Autoencoders on MNIST and OMNIGLOT

[java-deeplearning](https://github.com/wavelets/java-deeplearning) -

[jliszka.github.com](https://github.com/wavelets/jliszka.github.com) -

[jobtastic](https://github.com/wavelets/jobtastic) - Make your user-responsive long-running Celery jobs totally awesomer.

[js-must-watch](https://github.com/wavelets/js-must-watch) - Must-watch videos about javascript

[JSON-js](https://github.com/wavelets/JSON-js) - JSON in JavaScript

[kaggle-avito](https://github.com/wavelets/kaggle-avito) - Winning solution to the Avito CTR competition

[kaggle-blackbox](https://github.com/wavelets/kaggle-blackbox) - Deep learning made easy

[kaggle-burn-cpu](https://github.com/wavelets/kaggle-burn-cpu) - Code for the “Burn CPU, burn” competition at Kaggle. Uses Extreme Learning Machines and hyperopt.

[kaggle-cifar](https://github.com/wavelets/kaggle-cifar) - Code for the CIFAR-10 competition at Kaggle, uses cuda-convnet

[kaggle-cifar10](https://github.com/wavelets/kaggle-cifar10) - Code for Kaggle Cifar10 competition

[Kaggle-Competitions](https://github.com/wavelets/Kaggle-Competitions) - All Kaggle competitions

[kaggle-digits](https://github.com/wavelets/kaggle-digits) - Some code for the Digits competition at Kaggle, incl. pylearn2’s maxout

[kaggle-dogs-vs-cats](https://github.com/wavelets/kaggle-dogs-vs-cats) - Code for Kaggle Dovs vs. Cats competition

[kaggle-galaxies](https://github.com/wavelets/kaggle-galaxies) - Winning solution for the Galaxy Challenge on Kaggle (http://www.kaggle.com/c/galaxy-zoo-the-galaxy-challenge)

[kaggle-gender](https://github.com/wavelets/kaggle-gender) - A Kaggle competition: discriminate gender based on handwriting

[kaggle-marinexplore](https://github.com/wavelets/kaggle-marinexplore) -

[Kaggle-NDSB](https://github.com/wavelets/Kaggle-NDSB) - doc and model for NDSB

[kaggle-ndsb-1](https://github.com/wavelets/kaggle-ndsb-1) - Winning solution for the National Data Science Bowl competition on Kaggle (plankton classification)

[kaggle-solar-energy](https://github.com/wavelets/kaggle-solar-energy) -

[kaggle-stumbleupon](https://github.com/wavelets/kaggle-stumbleupon) - bag of words + sparsenn

[kaggle-whales](https://github.com/wavelets/kaggle-whales) - code for the Whale Detection Challenge competition on Kaggle

[kaggle_acquire-valued-shoppers-challenge](https://github.com/wavelets/kaggle_acquire-valued-shoppers-challenge) - Code for the Kaggle acquire valued shoppers challenge

[kaggle_diabetic_retinopathy](https://github.com/wavelets/kaggle_diabetic_retinopathy) - Fifth place solution of the Kaggle Diabetic Retinopathy competition.

[kaggle_insults](https://github.com/wavelets/kaggle_insults) - Kaggle Submission for “Detecting Insults in Social Commentary”

[Kalman-and-Bayesian-Filters-in-Python](https://github.com/wavelets/Kalman-and-Bayesian-Filters-in-Python) - Kalman Filter textbook using Ipython Notebook. This book takes a minimally mathematical approach, focusing on building intuition and experience, not formal proofs. Includes Kalman filters, Extended Kalman filters, unscented filters, and more. Includes exercises with solutions.

[kartograph.py](https://github.com/wavelets/kartograph.py) - Renders beautiful SVG maps in Python.

[kastnerkyle.github.io](https://github.com/wavelets/kastnerkyle.github.io) - Github blog

[Kayak](https://github.com/wavelets/Kayak) - Kayak is a library for automatic differentiation with applications to deep neural networks.

[kbmf](https://github.com/wavelets/kbmf) - Kernelized Bayesian Matrix Factorization

[keras](https://github.com/wavelets/keras) - Theano-based Deep Learning library

[klaus](https://github.com/wavelets/klaus) - A show-case of a state-of-the-art image classifier on iOS devices from libccv.org

[kod](https://github.com/wavelets/kod) - My personal code

[ladder](https://github.com/wavelets/ladder) - Ladder network is a deep learning algorithm that combines supervised and unsupervised learning

[lanyon](https://github.com/wavelets/lanyon) - markdown web server

[lda](https://github.com/wavelets/lda) - LDA topic modeling for node.js

[LearnDataScience](https://github.com/wavelets/LearnDataScience) - Open Content for self-directed learning in data science

[learnhaskell](https://github.com/wavelets/learnhaskell) - Learn Haskell

[learning-pymc](https://github.com/wavelets/learning-pymc) - Code I am using to learn PyMC

[learning-spark](https://github.com/wavelets/learning-spark) - A practical example of Apache Spark using the StackExchange dataset.

[leo-senate-model](https://github.com/wavelets/leo-senate-model) - Code and data for The Upshot’s Senate model.

[librosa](https://github.com/wavelets/librosa) - Python library for audio and music analysis

[libtins](https://github.com/wavelets/libtins) - High-level, multiplatform C++ network packet sniffing and crafting library.

[libvips](https://github.com/wavelets/libvips) - A fast image processing library with low memory needs.

[lifelines](https://github.com/wavelets/lifelines) - Survival analysis in Python

[lightfm](https://github.com/wavelets/lightfm) - A Python implementation of LightFM, a hybrid recommendation algorithm.

[lightning](https://github.com/wavelets/lightning) - Large-scale linear classification and regression in Python/Cython.

[LightTable](https://github.com/wavelets/LightTable) - The Light Table IDE

[lmkkmeans](https://github.com/wavelets/lmkkmeans) - Localized Multiple Kernel k-Means Clustering

[lstm-char-cnn](https://github.com/wavelets/lstm-char-cnn) - LSTM language model with CNN over characters

[luigi](https://github.com/wavelets/luigi) - Luigi is a Python module that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization etc. It also comes with Hadoop support built in.

[machine-learning](https://github.com/wavelets/machine-learning) - Code & Data for Introduction to Machine Learning with Scikit-Learn

[mailinabox](https://github.com/wavelets/mailinabox) - Mail-in-a-Box helps individuals take back control of their email by defining a one-click, easy-to-deploy SMTP+everything else server: a mail server in a box.

[mailr](https://github.com/wavelets/mailr) - Webmail client with gmail like conversations

[make](https://github.com/wavelets/make) - Source code for unix make

[math-as-code](https://github.com/wavelets/math-as-code) - a cheat-sheet for mathematical notation in code form

[matplotlib-gallery](https://github.com/wavelets/matplotlib-gallery) - Examples of matplotlib codes and plots

[MechanicalSoup](https://github.com/wavelets/MechanicalSoup) - A Python library for automating interaction with websites.

[meta](https://github.com/wavelets/meta) - A Modern C++ Data Sciences Toolkit

[metacademy-application](https://github.com/wavelets/metacademy-application) - Metacademy.org’s application code

[Metronome](https://github.com/wavelets/Metronome) - Suite of parallel iterative algorithms built on top of Iterative Reduce

[MFTracker](https://github.com/wavelets/MFTracker) - MF Tracker made in python using OpenCV 2.3.1 and SimpleCV

[Mining-the-Social-Web-2nd-Edition](https://github.com/wavelets/Mining-the-Social-Web-2nd-Edition) - The official online compendium for Mining the Social Web, 2nd Edition (O’Reilly, 2013)

[minirank](https://github.com/wavelets/minirank) - Ranking and ordinal regression algorithms in Python

[ml-ease](https://github.com/wavelets/ml-ease) - ADMM based large scale logistic regression

[mlb_terminal](https://github.com/wavelets/mlb_terminal) - A terminal interface for streaming real-time updates for MLB games

[mlp-character-recognition](https://github.com/wavelets/mlp-character-recognition) - Trains a multi-layer perceptron (MLP) neural network to perform optical character recognition (OCR).

[ML_for_Hackers](https://github.com/wavelets/ML_for_Hackers) - Code accompanying the book “Machine Learning for Hackers”

[mne-python-notebooks](https://github.com/wavelets/mne-python-notebooks) - IPython notebooks for EEG/MEG data processing using mne-python

[mocha](https://github.com/wavelets/mocha) - mocha - simple, flexible, fun javascript test framework for node.js & the browser. (BDD, TDD, QUnit styles via interfaces)

[mondrianforest](https://github.com/wavelets/mondrianforest) - Code for “Mondrian Forests: Efficient Online Random Forests”

[mongo-hdfs-export](https://github.com/wavelets/mongo-hdfs-export) -

[mongotools](https://github.com/wavelets/mongotools) - Wish MongoDB Tools

[morb](https://github.com/wavelets/morb) - Modular Restricted Boltzmann Machine (RBM) implementation using Theano

[morphing_faces](https://github.com/wavelets/morphing_faces) - Repository for the Morphing Faces demo

[mortar-recsys](https://github.com/wavelets/mortar-recsys) - A customizable recommendation engine for Hadoop and Pig by Mortar Data.

[Motivating_and_Visualizing_Recursion_in_Python](https://github.com/wavelets/Motivating_and_Visualizing_Recursion_in_Python) - Riffing on Gustavo Duarte’s arguments and examples about using tree traversal rather than factorial for motivating and visualizing recursion

[moviepy](https://github.com/wavelets/moviepy) - Script-based movie editing with python

[mpld3](https://github.com/wavelets/mpld3) - D3 Renderings of Matplotlib Graphics

[mrec](https://github.com/wavelets/mrec) -

[Multimedia-Machine-Learning-Tutorials](https://github.com/wavelets/Multimedia-Machine-Learning-Tutorials) -

[mxnet](https://github.com/wavelets/mxnet) - An efficient, flexible distributed framework for deep learning

[neupy](https://github.com/wavelets/neupy) - NeuPy is a Python library for Artificial Neural Networks.

[neural-networks](https://github.com/wavelets/neural-networks) - Artificial Neural Networks / Python

[neural-networks-and-deep-learning](https://github.com/wavelets/neural-networks-and-deep-learning) - Code samples for my book “Neural Networks and Deep Learning”

[neural-style](https://github.com/wavelets/neural-style) - Torch implementation of neural style algorithm

[neuraln](https://github.com/wavelets/neuraln) -

[neuraltalkTheano](https://github.com/wavelets/neuraltalkTheano) - Theano implementation of neuraltalk code by karpathy (https://github.com/karpathy/neuraltalk)

[neurokernel](https://github.com/wavelets/neurokernel) - Neurokernel Project

[neurosynth](https://github.com/wavelets/neurosynth) - NeuroSynth core tools

[nfldata](https://github.com/wavelets/nfldata) - Combining datasets with MapReduce on NFL play by play data.

[nfl_results](https://github.com/wavelets/nfl_results) - Results from NFL games since 1978 in CSV format

[ngcm_pandas_course](https://github.com/wavelets/ngcm_pandas_course) - Python data analysis course for 2015 NGCM Summer Academy

[ngs-course.github.io](https://github.com/wavelets/ngs-course.github.io) - NGS course

[ngxtop](https://github.com/wavelets/ngxtop) - Real-time metrics for nginx server

[nice](https://github.com/wavelets/nice) -

[nilearn](https://github.com/wavelets/nilearn) - Machine learning for NeuroImaging in Python

[NIPS2013_sklearn](https://github.com/wavelets/NIPS2013_sklearn) - Abstract for NIPS 2013

[nipspreview](https://github.com/wavelets/nipspreview) - Scripts that generate .html to more easily see NIPS papers

[nipy](https://github.com/wavelets/nipy) - Neuroimaging in python

[nntools](https://github.com/wavelets/nntools) - neural network tools for Theano

[nolearn](https://github.com/wavelets/nolearn) - scikit-learn compatible wrappers for neural net libraries, and other utilities.

[notebooks](https://github.com/wavelets/notebooks) - Some sample IPython notebooks for scikit-learn

[notes-on-neural-networks](https://github.com/wavelets/notes-on-neural-networks) - Rough working notes on neural networks

[nsq](https://github.com/wavelets/nsq) - A realtime distributed messaging platform

[nupic](https://github.com/wavelets/nupic) - Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms.

[nut](https://github.com/wavelets/nut) - Natural language Understanding Toolkit

[nye](https://github.com/wavelets/nye) - New Year’s Eve Cocktail Menu

[ocropy](https://github.com/wavelets/ocropy) - Python-based OCR package using recurrent neural networks.

[office-nfl-pool](https://github.com/wavelets/office-nfl-pool) - A fun introduction to Pandas andScikit-Learn using nfl data

[openfootball.github.io](https://github.com/wavelets/openfootball.github.io) - Open Football Data (football.db) Web Site

[openscoring](https://github.com/wavelets/openscoring) - REST web service for scoring PMML models

[OpenTLD](https://github.com/wavelets/OpenTLD) - Official source code for TLD

[OS-X-Yosemite-Security-and-Privacy-Guide](https://github.com/wavelets/OS-X-Yosemite-Security-and-Privacy-Guide) -

[OverFeat](https://github.com/wavelets/OverFeat) -

[PAC-Bayes_sample_compress_for_kernel_methods](https://github.com/wavelets/PAC-Bayes_sample_compress_for_kernel_methods) - Learning algorithms introduced in “A PAC-Bayes Sample Compression Approach to Kernel Methods” (ICML 2011)

[pandas-cookbook](https://github.com/wavelets/pandas-cookbook) - Recipes for using Python’s pandas library

[pandashells](https://github.com/wavelets/pandashells) - :panda_face: Bringing the python data stack to the shell prompt

[panns](https://github.com/wavelets/panns) - Python Approximate Nearest Neighbor Search in very high dimensional space with optimized indexing.

[parallel_ml_tutorial](https://github.com/wavelets/parallel_ml_tutorial) - Tutorial on scikit-learn and IPython for parallel machine learning

[pattern](https://github.com/wavelets/pattern) - Web mining module for Python, with tools for scraping, natural language processing, machine learning, network analysis and visualization.

[pattern_classification](https://github.com/wavelets/pattern_classification) - A collection of tutorials and examples for solving and understanding machine learning and pattern classification tasks

[PDA_Book](https://github.com/wavelets/PDA_Book) - Code Examples

[pgloader](https://github.com/wavelets/pgloader) - Loading data into PostgreSQL

[phraug](https://github.com/wavelets/phraug) - A set of simple Python scripts for pre-processing large files

[pmc](https://github.com/wavelets/pmc) - Probabilistic Multiplicity Counting

[pomegranate](https://github.com/wavelets/pomegranate) - Graphical models for Python, implemented in Cython for speed.

[portia](https://github.com/wavelets/portia) - Visual scraping for Scrapy

[pourover](https://github.com/wavelets/pourover) -

[ppod](https://github.com/wavelets/ppod) - Collection of snippets and what-not to share with others

[presentations](https://github.com/wavelets/presentations) - Presentations for JuliaCon

[psdash](https://github.com/wavelets/psdash) - A linux system information web dashboard using psutils and flask

[pybloomfiltermmap](https://github.com/wavelets/pybloomfiltermmap) - Fast Python Bloom Filter using Mmap

[pybo](https://github.com/wavelets/pybo) - Python package for modular Bayesian optimization

[pybrain](https://github.com/wavelets/pybrain) -

[pybrain-practice](https://github.com/wavelets/pybrain-practice) - A regression example for PyBrain

[pycaffe-recurrent](https://github.com/wavelets/pycaffe-recurrent) - IPython notebook for training multilayer LSTM and RNN networks with pycaffe

[pycon-pydata-sprint](https://github.com/wavelets/pycon-pydata-sprint) - Experimental work for using IPython.parallel with scikit-learn

[pyconsg2013-tut](https://github.com/wavelets/pyconsg2013-tut) - Introduction to data processing with Python

[pydata-gbrt-tutorial](https://github.com/wavelets/pydata-gbrt-tutorial) - IPython notebook for PyData SF 2014 tutorial: “Gradient Boosted Regression Trees in scikit-learn”

[PyData2014-Berlin](https://github.com/wavelets/PyData2014-Berlin) - Networks meet Finance in Python - July 27 2014

[pydata_ninja](https://github.com/wavelets/pydata_ninja) - The Path of the PyData Ninja

[pydub](https://github.com/wavelets/pydub) - Manipulate audio with a simple and easy high level interface

[pyGPs](https://github.com/wavelets/pyGPs) - pyGPs is a library containing an object-oriented python implementation for Gaussian Process (GP) regression and classification.

[pylearn2](https://github.com/wavelets/pylearn2) - A Machine Learning library based on Theano

[pymc](https://github.com/wavelets/pymc) - PyMC: Bayesian Stochastic Modelling in Python

[pymc-examples](https://github.com/wavelets/pymc-examples) - PyMC Example Notebooks

[pyneural](https://github.com/wavelets/pyneural) -

[PyPoi](https://github.com/wavelets/PyPoi) - “Py”thon program for “Poi”sson Image Editing

[pyston](https://github.com/wavelets/pyston) - Pyston is a new, open-source Python implementation using JIT techniques, being developed by Dropbox.

[pystruct](https://github.com/wavelets/pystruct) - Simple structured learning framework for python

[pytenn2014_tutorial](https://github.com/wavelets/pytenn2014_tutorial) - PyTennessee 2014: Statistical Data Analysis in Python

[python-bloomfilter](https://github.com/wavelets/python-bloomfilter) - Scalable Bloom Filter implemented in Python

[python-destin](https://github.com/wavelets/python-destin) - Python implementation of the DeSTIN deep learning perception system using the Theano library

[Python-for-Signal-Processing](https://github.com/wavelets/Python-for-Signal-Processing) - Notebooks for “Python for Signal Processing” book

[python-lda](https://github.com/wavelets/python-lda) - LDA in Python.

[python-recsys](https://github.com/wavelets/python-recsys) - A python library for implementing a recommender system

[PythonCounterPmf](https://github.com/wavelets/PythonCounterPmf) - Examples using Python’s Counter collection to implement a probability mass function (PMF)

[PythonicPerambulations](https://github.com/wavelets/PythonicPerambulations) - A port of jakevdp.github.com to the Pelican platform

[Python_for_Data_Science](https://github.com/wavelets/Python_for_Data_Science) - A rapid on-ramp primer for programmers who want to learn Python for doing data science research and development.

[python_reference](https://github.com/wavelets/python_reference) - Syntax examples for useful Python functions, methods, and modules

[pywt](https://github.com/wavelets/pywt) - PyWavelets - Discrete Wavelet Transform in Python

[quietnet](https://github.com/wavelets/quietnet) - Simple chat program using inaudible sounds and a computer’s microphone and speaker.

[rcnn](https://github.com/wavelets/rcnn) - R-CNN: Regions with Convolutional Neural Network Features

[recsys-mapreduce-mrjob](https://github.com/wavelets/recsys-mapreduce-mrjob) - Examples of Recommendations powered by MapReduce and mrjob

[reddit-bigquery](https://github.com/wavelets/reddit-bigquery) - Code + Jupyter notebook for analyzing and visualizing Reddit Data quickly and easily

[rendr](https://github.com/wavelets/rendr) - Render your Backbone.js apps on the client and the server, using Node.js.

[responses](https://github.com/wavelets/responses) - A utility for mocking out the Python Requests library.

[restricted-boltzmann-machines](https://github.com/wavelets/restricted-boltzmann-machines) - Restricted Boltzmann Machines in Python.

[rnn](https://github.com/wavelets/rnn) - Recurrent Neural Network library for Torch7’s nn

[RPi-KittyCam](https://github.com/wavelets/RPi-KittyCam) - Raspberry Pi app using a camera and PIR motion sensor, written in Node.js with Johnny-Five and Kittydar for cat facial detection

[sarah-palin-lda](https://github.com/wavelets/sarah-palin-lda) - Topic Modeling the Sarah Palin emails.

[scientific-python-intro-45mins](https://github.com/wavelets/scientific-python-intro-45mins) - An intro presentation of scientific Python in 45 minutes

[scikit-fuzzy](https://github.com/wavelets/scikit-fuzzy) - Fuzzy Logic SciKit (Toolkit for SciPy)

[scikit-image](https://github.com/wavelets/scikit-image) - Image Processing SciKit (Toolbox for SciPy)

[scikit-learn-tutorial](https://github.com/wavelets/scikit-learn-tutorial) - Applied Machine Learning in Python with scikit-learn

[scikits.cuda](https://github.com/wavelets/scikits.cuda) - CUDA SciKit

[scipy](https://github.com/wavelets/scipy) - Scipy main repository

[scipy-lecture-notes](https://github.com/wavelets/scipy-lecture-notes) - Tutorial material on the scientific Python ecosystem

[scipy-tutorials](https://github.com/wavelets/scipy-tutorials) - SciPy tutorials. This is outdated, check out scipy-lecture-notes

[scipy_2015_sklearn_tutorial](https://github.com/wavelets/scipy_2015_sklearn_tutorial) - Scikit-Learn tutorial material for Scipy 2015

[scipy_proceedings](https://github.com/wavelets/scipy_proceedings) - Tools used to generate the SciPy conference proceedings

[SCRNNs](https://github.com/wavelets/SCRNNs) - This is a self contained software accompanying the paper titled: Learning Longer Memory in Recurrent Neural Networks: http://arxiv.org/abs/1412.7753.

[seaborn](https://github.com/wavelets/seaborn) - Statistical data visualization using matplotlib

[SeattleBike](https://github.com/wavelets/SeattleBike) - Understanding Seattle Bike Count data

[secondorderdemos](https://github.com/wavelets/secondorderdemos) - second order demos

[secrets-newsblur-skel](https://github.com/wavelets/secrets-newsblur-skel) - Project to get NewsBlur running on a cluster of VMs

[sentence2vec](https://github.com/wavelets/sentence2vec) - Tools for mapping a sentence with arbitrary length to vector space

[sentiment-analyzer](https://github.com/wavelets/sentiment-analyzer) - Tweets Sentiment Analyzer

[sentiment_analysis_python](https://github.com/wavelets/sentiment_analysis_python) - Working with sentiment analysis in Python.

[server-configs-nginx](https://github.com/wavelets/server-configs-nginx) - Nginx HTTP server boilerplate configs

[shotcut](https://github.com/wavelets/shotcut) - cross-platform (Qt), open-source (GPLv3) video editor

[sigma.js](https://github.com/wavelets/sigma.js) - A JavaScript library dedicated to graph drawing

[simhash-py](https://github.com/wavelets/simhash-py) - Simhash and near-duplicate detection

[SimpleAintEasy](https://github.com/wavelets/SimpleAintEasy) - A compendium of the pitfalls and problems that arise when using standard statistical methods

[SimpleCV](https://github.com/wavelets/SimpleCV) - The Open Source Framework for Machine Vision

[skdata](https://github.com/wavelets/skdata) - Data sets for machine learning in Python

[skimage-tutorials](https://github.com/wavelets/skimage-tutorials) - Scikit-image tutorials

[skip-thoughts](https://github.com/wavelets/skip-thoughts) - Sent2Vec encoder and training code from the paper “Skip-Thought Vectors”

[sklearn-theano](https://github.com/wavelets/sklearn-theano) - Scikit-learn compatible tools using theano

[sklearn_pycon2013](https://github.com/wavelets/sklearn_pycon2013) - Files for my scikit-learn tutorial at PyCon 2013

[sklearn_pycon2014](https://github.com/wavelets/sklearn_pycon2014) - Repository containing files for my PyCon 2014 scikit-learn tutorial.

[sklearn_scipy2013](https://github.com/wavelets/sklearn_scipy2013) - Scikit-learn tutorials for the Scipy 2013 conference

[sklearn_tutorial](https://github.com/wavelets/sklearn_tutorial) - Intro to machine learning with sklearn

[skll](https://github.com/wavelets/skll) - SciKit-Learn Laboratory makes it easy to run machine learning experiments.

[smc-challenge](https://github.com/wavelets/smc-challenge) -

[smile](https://github.com/wavelets/smile) - Statistical Machine Intelligence & Learning Engine

[snake-charmer](https://github.com/wavelets/snake-charmer) - A self-contained Python workbench for scientific programming, data mining, maths, stats and visualization

[snakebite](https://github.com/wavelets/snakebite) - A pure python HDFS client

[snap](https://github.com/wavelets/snap) - Stanford Network Analysis Platform (SNAP) is a general purpose network analysis and graph mining library.

[spaCy](https://github.com/wavelets/spaCy) - Industrial strength NLP with Python and Cython

[spark-movie-lens](https://github.com/wavelets/spark-movie-lens) - An on-line movie recommender using Spark, Python, and the MovieLens dataset

[SparseConvNet](https://github.com/wavelets/SparseConvNet) - Spatially-sparse convolutional networks. Allows processing of sparse 2, 3 and 4 dimensional data.Build CNNs on the square/cubic/hypercubic or triangular/tetrahedral/hyper-tetrahedral lattices.

[spearmint](https://github.com/wavelets/spearmint) - Spearmint is a package to perform Bayesian optimization according to the algorithms outlined in the paper: Practical Bayesian Optimization of Machine Learning Algorithms. Jasper Snoek, Hugo Larochelle and Ryan P. Adams. Advances in Neural Information Processing Systems, 2012

[speech-language-processing](https://github.com/wavelets/speech-language-processing) - A curated list of speech and natural language processing resources

[spylearn](https://github.com/wavelets/spylearn) - Repo for experiments on pyspark and sklearn

[Squire](https://github.com/wavelets/Squire) - HTML5 rich text editor

[sr-captcha](https://github.com/wavelets/sr-captcha) -

[stardose](https://github.com/wavelets/stardose) - A recommender system for GitHub repositories

[stat-learning](https://github.com/wavelets/stat-learning) - Notes and exercise attempts for “An Introduction to Statistical Learning”

[statistical-analysis-python-tutorial](https://github.com/wavelets/statistical-analysis-python-tutorial) - Statistical Data Analysis in Python

[statlearning-notebooks](https://github.com/wavelets/statlearning-notebooks) - Python notebooks for exercises covered in Stanford statlearning class (where exercises were in R).

[statsintro](https://github.com/wavelets/statsintro) - Introduction to Statistics

[statsmodels](https://github.com/wavelets/statsmodels) - Statsmodels: statistical modeling and econometrics in Python

[streamparse](https://github.com/wavelets/streamparse) - streamparse lets you run Python code against real-time streams of data. Integrates with Apache Storm.

[streamtools](https://github.com/wavelets/streamtools) - tools for working with streams of data

[swift-cheat-sheet](https://github.com/wavelets/swift-cheat-sheet) - A short guide to using Apple’s new programming language, Swift.

[syncthing](https://github.com/wavelets/syncthing) - Open Source Continuous Replication / Cluster Synchronization Thing

[talk-pydata2015](https://github.com/wavelets/talk-pydata2015) -

[talks](https://github.com/wavelets/talks) - IPython notebooks and slides for talks I’ve given

[tan-clustering](https://github.com/wavelets/tan-clustering) - Hierarchical word clustering, following “Brown clustering” (Brown et al., 1992)

[tauCharts](https://github.com/wavelets/tauCharts) - D3 based data-focused charting library. Designed with passion. Flexible.

[TextBlob](https://github.com/wavelets/TextBlob) - Simple, Pythonic, text processing–Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

[textract](https://github.com/wavelets/textract) - extract text from any document. no muss. no fuss.

[textures](https://github.com/wavelets/textures) - Textures.js is a JavaScript library for creating SVG patterns

[Theano-Lights](https://github.com/wavelets/Theano-Lights) - Deep learning research framework based on Theano

[theano-tutorial](https://github.com/wavelets/theano-tutorial) - A collection of tutorials on neural networks, using Theano

[Theano-Tutorials](https://github.com/wavelets/Theano-Tutorials) - Bare bones introduction to machine learning from linear regression to convolutional neural networks using Theano.

[ThinkBayes](https://github.com/wavelets/ThinkBayes) - Code repository for Think Bayes.

[ThinkStats2](https://github.com/wavelets/ThinkStats2) - Text and supporting code for Think Stats, 2nd Edition

[thumbor](https://github.com/wavelets/thumbor) - thumbor is an open-source photo thumbnail service by globo.com

[toolz](https://github.com/wavelets/toolz) - A functional standard library for Python.

[trackpy](https://github.com/wavelets/trackpy) - Python particle tracking toolkit

[trackpy-examples](https://github.com/wavelets/trackpy-examples) - sample images, examples, and speed tests for trackpy

[trials](https://github.com/wavelets/trials) - Tiny Bayesian A/B testing library

[tutorial_ml_gkbionics](https://github.com/wavelets/tutorial_ml_gkbionics) - A Tutorial on Simple Machine Learning Methods Held for the Graduate School on Bionics, 2012

[twisted](https://github.com/wavelets/twisted) - Event-driven networking engine written in Python.

[ufldl_tutorial](https://github.com/wavelets/ufldl_tutorial) - Stanford Unsupervised Feature Learning and Deep Learning Tutorial

[universal-jst](https://github.com/wavelets/universal-jst) - precompiling your templates into JST’s, with some sugar

[us-address-parser](https://github.com/wavelets/us-address-parser) - US address parsing

[Variational-Autoencoder](https://github.com/wavelets/Variational-Autoencoder) - Implementation of a variational Auto-encoder

[videogrep](https://github.com/wavelets/videogrep) -

[video_cnn](https://github.com/wavelets/video_cnn) - CS231N Final Project - Andrew Giel, Ryan Diaz

[visual-semantic-embedding](https://github.com/wavelets/visual-semantic-embedding) - Implementation of the image-sentence embedding method described in “Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models”

[vowpal_wabbit](https://github.com/wavelets/vowpal_wabbit) - John Langford’s original release of Vowpal Wabbit – a fast online learning algorithm

[waifu2x](https://github.com/wavelets/waifu2x) - Image Super-Resolution for Anime/Fan-Art

[wand](https://github.com/wavelets/wand) - The ctypes-based simple ImageMagick binding for Python

[watchy](https://github.com/wavelets/watchy) - Stats aggregation and embeddable client library - inspired from StatsD

[wifite](https://github.com/wavelets/wifite) -

[wikichallenge](https://github.com/wavelets/wikichallenge) - An implementation of Dell Zhang’s solution to Wikipedia’s Participation Challenge on Kaggle

[wiwinwlh](https://github.com/wavelets/wiwinwlh) - What I Wish I Knew When Learning Haskell

[word2vec](https://github.com/wavelets/word2vec) - Python interface to Google word2vec

[Word2VecExample](https://github.com/wavelets/Word2VecExample) - An example application using Word2Vec. Given a list of words, it finds the one which isn’t ‘like’ the others - a typical language understanding evaluation task.

[xgboost](https://github.com/wavelets/xgboost) - eXtreme Gradient Boosting (Tree) Library

[xmltodict](https://github.com/wavelets/xmltodict) - Python module that makes working with XML feel like you are working with JSON

[zipline](https://github.com/wavelets/zipline) - Zipline, a Pythonic Algorithmic Trading Library
zipline - Zipline, a Pythonic Algorithmic Trading Library

Thanks Joseph Misiti

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