diff --git a/README.md b/README.md index 900aa8c..81f6f17 100644 --- a/README.md +++ b/README.md @@ -3,7 +3,7 @@ A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php. If you want to contribute to this list (please do), send me a pull request or contact me [@josephmisiti](https://twitter.com/josephmisiti) -Also, when you noticed that listed repository should be deprecated. +Also, a listed repository should be deprecated if: * Repository's owner explicitly say that "this library is not maintained". * Not committed for long time (2~3 years). @@ -14,6 +14,8 @@ For a list of free machine learning books available for download, go [here](http +- [APL](#apl) + - [General-Purpose Machine Learning](#apl-general-purpose) - [C](#c) - [General-Purpose Machine Learning](#c-general-purpose) - [Computer Vision](#c-cv) @@ -29,6 +31,9 @@ For a list of free machine learning books available for download, go [here](http - [Natural Language Processing](#clojure-nlp) - [General-Purpose Machine Learning](#clojure-general-purpose) - [Data Analysis / Data Visualization](#clojure-data-analysis) +- [Elixir](#elixir) + - [General-Purpose Machine Learning](#elixir-general-purpose) + - [Natural Language Processing](#elixir-nlp) - [Erlang](#erlang) - [General-Purpose Machine Learning](#erlang-general-purpose) - [Go](#go) @@ -79,6 +84,7 @@ For a list of free machine learning books available for download, go [here](http - [Data Analysis / Data Visualization](#python-data-analysis) - [Misc Scripts / iPython Notebooks / Codebases](#python-misc) - [Kaggle Competition Source Code](#python-kaggle) + - [Neural networks](#python-neural networks) - [Ruby](#ruby) - [Natural Language Processing](#ruby-nlp) - [General-Purpose Machine Learning](#ruby-general-purpose) @@ -89,7 +95,7 @@ For a list of free machine learning books available for download, go [here](http - [R](#r) - [General-Purpose Machine Learning](#r-general-purpose) - [Data Analysis / Data Visualization](#r-data-analysis) -- [SAS] (#sas) +- [SAS](#sas) - [General-Purpose Machine Learning] (#sas-general-purpose) - [Data Analysis / Data Visualization] (#sas-data-analysis) - [High Performance Machine Learning (MPP)] (#sas-mpp) @@ -101,10 +107,19 @@ For a list of free machine learning books available for download, go [here](http - [General-Purpose Machine Learning](#scala-general-purpose) - [Swift](#swift) - [General-Purpose Machine Learning](#swift-general-purpose) +- [TensorFlow](#tensor) + - [General-Purpose Machine Learning](#tensor-general-purpose) - [Credits](#credits) + +## APL + + +#### General-Purpose Machine Learning +* [naive-apl](https://github.com/mattcunningham/naive-apl) - Naive Bayesian Classifier implementation in APL + ## C @@ -137,10 +152,11 @@ For a list of free machine learning books available for download, go [here](http #### General-Purpose Machine Learning +* [ROOT](https://root.cern.ch) - A modular scientific software framework. It provides all the functionalities needed to deal with big data processing, statistical analysis, visualization and storage. * [mlpack](http://www.mlpack.org/) - A scalable C++ machine learning library * [DLib](http://dlib.net/ml.html) - A suite of ML tools designed to be easy to imbed in other applications * [encog-cpp](https://code.google.com/p/encog-cpp/) -* [shark](http://image.diku.dk/shark/sphinx_pages/build/html/index.html) +* [shark](http://image.diku.dk/shark/sphinx_pages/build/html/index.html) - A fast, modular, feature-rich open-source C++ machine learning library. * [Vowpal Wabbit (VW)](https://github.com/JohnLangford/vowpal_wabbit/wiki) - A fast out-of-core learning system. * [sofia-ml](https://code.google.com/p/sofia-ml/) - Suite of fast incremental algorithms. * [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox @@ -159,6 +175,10 @@ For a list of free machine learning books available for download, go [here](http * [Fido](https://github.com/FidoProject/Fido) - A highly-modular C++ machine learning library for embedded electronics and robotics. * [LightGBM](https://github.com/Microsoft/LightGBM) - Microsoft's fast, distributed, high performance gradient boosting (GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks. * [DyNet](https://github.com/clab/dynet) - A dynamic neural network library working well with networks that have dynamic structures that change for every training instance. Written in C++ with bindings in Python. +* [DSSTNE](https://github.com/amznlabs/amazon-dsstne) - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility. +* [Intel(R) DAAL](https://github.com/01org/daal) - A high performance software library developed by Intel and optimized for Intel's architectures. Library provides algorithmic building blocks for all stages of data analytics and allows to process data in batch, online and distributed modes. +* [MLDB](http://mldb.ai) - The Machine Learning Database is a database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs. +* [Regularized Greedy Forest](http://stat.rutgers.edu/home/tzhang/software/rgf/) - Regularized greedy forest (RGF) tree ensemble learning method. #### Natural Language Processing @@ -214,6 +234,8 @@ For a list of free machine learning books available for download, go [here](http * [Statistiker](https://github.com/clojurewerkz/statistiker) - Basic Machine Learning algorithms in Clojure. * [clortex](https://github.com/nupic-community/clortex) - General Machine Learning library using Numenta’s Cortical Learning Algorithm * [comportex](https://github.com/nupic-community/comportex) - Functionally composable Machine Learning library using Numenta’s Cortical Learning Algorithm +* [cortex](https://github.com/thinktopic/cortex) - Neural networks, regression and feature learning in Clojure. +* [lambda-ml](https://github.com/cloudkj/lambda-ml) - Simple, concise implementations of machine learning techniques and utilities in Clojure. #### Data Analysis / Data Visualization @@ -222,11 +244,25 @@ For a list of free machine learning books available for download, go [here](http * [PigPen](https://github.com/Netflix/PigPen) - Map-Reduce for Clojure. * [Envision](https://github.com/clojurewerkz/envision) - Clojure Data Visualisation library, based on Statistiker and D3 + +## Elixir + + +#### General-Purpose Machine Learning + +* [Simple Bayes](https://github.com/fredwu/simple_bayes) - A Simple Bayes / Naive Bayes implementation in Elixir. + + +#### Natural Language Processing + +* [Stemmer](https://github.com/fredwu/stemmer) - An English (Porter2) stemming implementation in Elixir. + ## Erlang #### General-Purpose Machine Learning + * [Disco](https://github.com/discoproject/disco/) - Map Reduce in Erlang @@ -243,6 +279,7 @@ For a list of free machine learning books available for download, go [here](http #### General-Purpose Machine Learning +* [gago](https://github.com/MaxHalford/gago) - Multi-population, flexible, parallel genetic algorithm. * [Go Learn](https://github.com/sjwhitworth/golearn) - Machine Learning for Go * [go-pr](https://github.com/daviddengcn/go-pr) - Pattern recognition package in Go lang. * [go-ml](https://github.com/alonsovidales/go_ml) - Linear / Logistic regression, Neural Networks, Collaborative Filtering and Gaussian Multivariate Distribution @@ -298,6 +335,7 @@ For a list of free machine learning books available for download, go [here](http * [ClearTK](https://code.google.com/p/cleartk/) - ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA. * [Apache cTAKES](http://ctakes.apache.org/) - Apache clinical Text Analysis and Knowledge Extraction System (cTAKES) is an open-source natural language processing system for information extraction from electronic medical record clinical free-text. * [ClearNLP](http://www.clearnlp.com) - The ClearNLP project provides software and resources for natural language processing. The project started at the Center for Computational Language and EducAtion Research, and is currently developed by the Center for Language and Information Research at Emory University. This project is under the Apache 2 license. +* [CogcompNLP](https://github.com/IllinoisCogComp/illinois-cogcomp-nlp) - This project collects a number of core libraries for Natural Language Processing (NLP) developed in the University of Illinois' Cognitive Computation Group, for example `illinois-core-utilities` which provides a set of NLP-friendly data structures and a number of NLP-related utilities that support writing NLP applications, running experiments, etc, `illinois-edison` a library for feature extraction from illinois-core-utilities data structures and many other packages. #### General-Purpose Machine Learning @@ -306,13 +344,10 @@ For a list of free machine learning books available for download, go [here](http * [Datumbox](https://github.com/datumbox/datumbox-framework) - Machine Learning framework for rapid development of Machine Learning and Statistical applications * [ELKI](http://elki.dbs.ifi.lmu.de/) - Java toolkit for data mining. (unsupervised: clustering, outlier detection etc.) * [Encog](https://github.com/encog/encog-java-core) - An advanced neural network and machine learning framework. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using multithreaded resilient propagation. Encog can also make use of a GPU to further speed processing time. A GUI based workbench is also provided to help model and train neural networks. -* [EvA2](www.ra.cs.uni-tuebingen.de/software/eva2/) - Evolutionary Algorithms Framework with Genetic Algorithm, Differential Evolution, Particle Swarm Optimization, Evolution Strategies, Covariance Matrix Adaptation Evolution Strategy, and more * [FlinkML in Apache Flink](https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/libs/ml/index.html) - Distributed machine learning library in Flink * [H2O](https://github.com/h2oai/h2o-3) - ML engine that supports distributed learning on Hadoop, Spark or your laptop via APIs in R, Python, Scala, REST/JSON. * [htm.java](https://github.com/numenta/htm.java) - General Machine Learning library using Numenta’s Cortical Learning Algorithm * [java-deeplearning](https://github.com/deeplearning4j/deeplearning4j) - Distributed Deep Learning Platform for Java, Clojure,Scala -* [JAVA-ML](http://java-ml.sourceforge.net/) - A general ML library with a common interface for all algorithms in Java -* [JSAT](https://code.google.com/p/java-statistical-analysis-tool/) - Numerous Machine Learning algorithms for classification, regression, and clustering. * [Mahout](https://github.com/apache/mahout) - Distributed machine learning * [Meka](http://meka.sourceforge.net/) - An open source implementation of methods for multi-label classification and evaluation (extension to Weka). * [MLlib in Apache Spark](http://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark @@ -327,6 +362,8 @@ For a list of free machine learning books available for download, go [here](http * [SystemML](https://github.com/apache/incubator-systemml) - flexible, scalable machine learning (ML) language. * [WalnutiQ](https://github.com/WalnutiQ/WalnutiQ) - object oriented model of the human brain * [Weka](http://www.cs.waikato.ac.nz/ml/weka/) - Weka is a collection of machine learning algorithms for data mining tasks +* [LBJava](https://github.com/IllinoisCogComp/lbjava/) - Learning Based Java is a modeling language for the rapid development of software systems, offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer's application. + #### Speech Recognition * [CMU Sphinx](http://cmusphinx.sourceforge.net/) - Open Source Toolkit For Speech Recognition purely based on Java speech recognition library. @@ -379,6 +416,7 @@ For a list of free machine learning books available for download, go [here](http * [Z3d](https://github.com/NathanEpstein/Z3d) - Easily make interactive 3d plots built on Three.js * [Sigma.js](http://sigmajs.org/) - JavaScript library dedicated to graph drawing. * [C3.js](http://c3js.org/)- customizable library based on D3.js for easy chart drawing. +* [Datamaps](http://datamaps.github.io/)- Customizable SVG map/geo visualizations using D3.js. * [ZingChart](http://www.zingchart.com/)- library written on Vanilla JS for big data visualization. * [cheminfo](http://www.cheminfo.org/) - Platform for data visualization and analysis, using the [visualizer](https://github.com/npellet/visualizer) project. @@ -390,6 +428,7 @@ For a list of free machine learning books available for download, go [here](http * [Clusterfck](http://harthur.github.io/clusterfck/) - Agglomerative hierarchical clustering implemented in Javascript for Node.js and the browser * [Clustering.js](https://github.com/emilbayes/clustering.js) - Clustering algorithms implemented in Javascript for Node.js and the browser * [Decision Trees](https://github.com/serendipious/nodejs-decision-tree-id3) - NodeJS Implementation of Decision Tree using ID3 Algorithm +* [DN2A](https://github.com/dn2a/dn2a-javascript) - Digital Neural Networks Architecture * [figue](http://code.google.com/p/figue/) - K-means, fuzzy c-means and agglomerative clustering * [Node-fann](https://github.com/rlidwka/node-fann) - FANN (Fast Artificial Neural Network Library) bindings for Node.js * [Kmeans.js](https://github.com/emilbayes/kMeans.js) - Simple Javascript implementation of the k-means algorithm, for node.js and the browser @@ -452,6 +491,10 @@ For a list of free machine learning books available for download, go [here](http * [ManifoldLearning](https://github.com/wildart/ManifoldLearning.jl) - A Julia package for manifold learning and nonlinear dimensionality reduction * [MXNet](https://github.com/dmlc/mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more. * [Merlin](https://github.com/hshindo/Merlin.jl) - Flexible Deep Learning Framework in Julia +* [ROCAnalysis](https://github.com/davidavdav/ROCAnalysis.jl) - Receiver Operating Characteristics and functions for evaluation probabilistic binary classifiers +* [GaussianMixtures] (https://github.com/davidavdav/GaussianMixtures.jl) - Large scale Gaussian Mixture Models +* [ScikitLearn] (https://github.com/cstjean/ScikitLearn.jl) - Julia implementation of the scikit-learn API +* [Knet](https://github.com/denizyuret/Knet.jl) - Koç University Deep Learning Framework #### Natural Language Processing @@ -499,6 +542,7 @@ For a list of free machine learning books available for download, go [here](http * [randomkit](http://jucor.github.io/torch-randomkit/) - Numpy's randomkit, wrapped for Torch * [signal](http://soumith.ch/torch-signal/signal/) - A signal processing toolbox for Torch-7. FFT, DCT, Hilbert, cepstrums, stft * [nn](https://github.com/torch/nn) - Neural Network package for Torch + * [torchnet](https://github.com/torchnet/torchnet) - framework for torch which provides a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming * [nngraph](https://github.com/torch/nngraph) - This package provides graphical computation for nn library in Torch7. * [nnx](https://github.com/clementfarabet/lua---nnx) - A completely unstable and experimental package that extends Torch's builtin nn library * [rnn](https://github.com/Element-Research/rnn) - A Recurrent Neural Network library that extends Torch's nn. RNNs, LSTMs, GRUs, BRNNs, BLSTMs, etc. @@ -665,6 +709,7 @@ on MNIST digits[DEEP LEARNING] ### General-Purpose Machine Learning +* [PHP-ML](https://github.com/php-ai/php-ml) - Machine Learning library for PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library. * [PredictionBuilder](https://github.com/denissimon/prediction-builder) - A library for machine learning that builds predictions using a linear regression. @@ -677,6 +722,7 @@ on MNIST digits[DEEP LEARNING] * [SimpleCV](http://simplecv.org/) - An open source computer vision framework that gives access to several high-powered computer vision libraries, such as OpenCV. Written on Python and runs on Mac, Windows, and Ubuntu Linux. * [Vigranumpy](https://github.com/ukoethe/vigra) - Python bindings for the VIGRA C++ computer vision library. * [OpenFace](https://cmusatyalab.github.io/openface/) - Free and open source face recognition with deep neural networks. +* [PCV](https://github.com/jesolem/PCV) - Open source Python module for computer vision #### Natural Language Processing @@ -702,6 +748,12 @@ on MNIST digits[DEEP LEARNING] * [colibri-core](https://github.com/proycon/colibri-core) - Python binding to C++ library for extracting and working with with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way. * [spaCy](https://github.com/honnibal/spaCy/) - Industrial strength NLP with Python and Cython. * [PyStanfordDependencies](https://github.com/dmcc/PyStanfordDependencies) - Python interface for converting Penn Treebank trees to Stanford Dependencies. +* [Distance](https://github.com/doukremt/distance) - Levenshtein and Hamming distance computation +* [Fuzzy Wuzzy](https://github.com/seatgeek/fuzzywuzzy) - Fuzzy String Matching in Python +* [jellyfish](https://github.com/jamesturk/jellyfish) - a python library for doing approximate and phonetic matching of strings. +* [editdistance](https://pypi.python.org/pypi/editdistance) - fast implementation of edit distance +* [textacy](https://github.com/chartbeat-labs/textacy) - higher-level NLP built on Spacy +* [stanford-corenlp-python](https://github.com/dasmith/stanford-corenlp-python) - Python wrapper for [Stanford CoreNLP](https://github.com/stanfordnlp/CoreNLP) #### General-Purpose Machine Learning @@ -720,6 +772,7 @@ on MNIST digits[DEEP LEARNING] * [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing. * [Pylearn2](https://github.com/lisa-lab/pylearn2) - A Machine Learning library based on [Theano](https://github.com/Theano/Theano). * [keras](https://github.com/fchollet/keras) - Modular neural network library based on [Theano](https://github.com/Theano/Theano). +* [Lasagne](https://github.com/Lasagne/Lasagne) - Lightweight library to build and train neural networks in Theano. * [hebel](https://github.com/hannes-brt/hebel) - GPU-Accelerated Deep Learning Library in Python. * [Chainer](https://github.com/pfnet/chainer) - Flexible neural network framework * [gensim](https://github.com/piskvorky/gensim) - Topic Modelling for Humans. @@ -733,6 +786,7 @@ on MNIST digits[DEEP LEARNING] * [Bolt](https://github.com/pprett/bolt) - Bolt Online Learning Toolbox * [CoverTree](https://github.com/patvarilly/CoverTree) - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree * [nilearn](https://github.com/nilearn/nilearn) - Machine learning for NeuroImaging in Python +* [imbalanced-learn](http://contrib.scikit-learn.org/imbalanced-learn/) - Python module to perform under sampling and over sampling with various techniques. * [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox * [Pyevolve](https://github.com/perone/Pyevolve) - Genetic algorithm framework. * [Caffe](http://caffe.berkeleyvision.org) - A deep learning framework developed with cleanliness, readability, and speed in mind. @@ -762,6 +816,9 @@ on MNIST digits[DEEP LEARNING] * [MXNet](https://github.com/dmlc/mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more. * [milk](https://github.com/luispedro/milk) - Machine learning toolkit focused on supervised classification. * [TFLearn](https://github.com/tflearn/tflearn) - Deep learning library featuring a higher-level API for TensorFlow. +* [REP](https://github.com/yandex/rep) - an IPython-based environment for conducting data-driven research in a consistent and reproducible way. REP is not trying to substitute scikit-learn, but extends it and provides better user experience. +* [rgf_python](https://github.com/fukatani/rgf_python) - Python bindings for Regularized Greedy Forest (Tree) Library. +* [gym](https://github.com/openai/gym) - OpenAI Gym is a toolkit for developing and comparing reinforcement learning algorithms. #### Data Analysis / Data Visualization @@ -784,6 +841,7 @@ on MNIST digits[DEEP LEARNING] * [plotly](https://plot.ly/python/) - Collaborative web plotting for Python and matplotlib. * [vincent](https://github.com/wrobstory/vincent) - A Python to Vega translator. * [d3py](https://github.com/mikedewar/d3py) - A plotting library for Python, based on [D3.js](http://d3js.org/). +* [PyDexter](https://github.com/D3xterjs/pydexter) - Simple plotting for Python. Wrapper for D3xterjs; easily render charts in-browser. * [ggplot](https://github.com/yhat/ggplot) - Same API as ggplot2 for R. * [ggfortify](https://github.com/sinhrks/ggfortify) - Unified interface to ggplot2 popular R packages. * [Kartograph.py](https://github.com/kartograph/kartograph.py) - Rendering beautiful SVG maps in Python. @@ -803,6 +861,10 @@ on MNIST digits[DEEP LEARNING] * [pastalog](https://github.com/rewonc/pastalog) - Simple, realtime visualization of neural network training performance. * [caravel](https://github.com/airbnb/caravel) - A data exploration platform designed to be visual, intuitive, and interactive. * [Dora](https://github.com/nathanepstein/dora) - Tools for exploratory data analysis in Python. +* [Ruffus](http://www.ruffus.org.uk) - Computation Pipeline library for python. +* [SOMPY](https://github.com/sevamoo/SOMPY) - Self Organizing Map written in Python (Uses neural networks for data analysis). +* [HDBScan](https://github.com/lmcinnes/hdbscan) - implementation of the hdbscan algorithm in Python - used for clustering +* [visualize_ML](https://github.com/ayush1997/visualize_ML) - A python package for data exploration and data analysis. #### Misc Scripts / iPython Notebooks / Codebases @@ -839,10 +901,20 @@ on MNIST digits[DEEP LEARNING] * [Dive into Machine Learning with Python Jupyter notebook and scikit-learn](https://github.com/hangtwenty/dive-into-machine-learning) - "I learned Python by hacking first, and getting serious *later.* I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself." * [TDB](https://github.com/ericjang/tdb) - TensorDebugger (TDB) is a visual debugger for deep learning. It features interactive, node-by-node debugging and visualization for TensorFlow. * [Suiron](https://github.com/kendricktan/suiron/) - Machine Learning for RC Cars. +* [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos) - IPython notebooks from Data School's video tutorials on scikit-learn. +* [Practical XGBoost in Python](http://education.parrotprediction.teachable.com/courses/practical-xgboost-in-python) - comprehensive online course about using XGBoost in Python + + +#### Neural networks +* [Neural networks](https://github.com/karpathy/neuraltalk) - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. +* [Neuron](https://github.com/molcik/Neuron) - Neuron is simple class for time series predictions. It's utilize LNU (Linear Neural Unit), QNU (Quadratic Neural Unit), RBF (Radial Basis Function), MLP (Multi Layer Perceptron), MLP-ELM (Multi Layer Perceptron - Extreme Learning Machine) neural networks learned with Gradient descent or LeLevenberg–Marquardt algorithm. + #### Kaggle Competition Source Code + + * [wiki challenge](https://github.com/hammer/wikichallenge) - An implementation of Dell Zhang's solution to Wikipedia's Participation Challenge on Kaggle * [kaggle insults](https://github.com/amueller/kaggle_insults) - Kaggle Submission for "Detecting Insults in Social Commentary" * [kaggle_acquire-valued-shoppers-challenge](https://github.com/MLWave/kaggle_acquire-valued-shoppers-challenge) - Code for the Kaggle acquire valued shoppers challenge @@ -912,7 +984,7 @@ on MNIST digits[DEEP LEARNING] * [deeplearn-rs](https://github.com/tedsta/deeplearn-rs) - deeplearn-rs provides simple networks that use matrix multiplication, addition, and ReLU under the MIT license. * [rustlearn](https://github.com/maciejkula/rustlearn) - a machine learning framework featuring logistic regression, support vector machines, decision trees and random forests. * [rusty-machine](https://github.com/AtheMathmo/rusty-machine) - a pure-rust machine learning library. -* [leaf](https://github.com/autumnai/leaf) - open source framework for machine intelligence, sharing concepts from TensorFlow and Caffe. Available under the MIT license. +* [leaf](https://github.com/autumnai/leaf) - open source framework for machine intelligence, sharing concepts from TensorFlow and Caffe. Available under the MIT license. [**[Deprecated]**](https://medium.com/@mjhirn/tensorflow-wins-89b78b29aafb#.s0a3uy4cc) * [RustNN](https://github.com/jackm321/RustNN) - RustNN is a feedforward neural network library. @@ -941,6 +1013,8 @@ on MNIST digits[DEEP LEARNING] * [elasticnet](http://cran.r-project.org/web/packages/elasticnet/index.html) - elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA * [ElemStatLearn](http://cran.r-project.org/web/packages/ElemStatLearn/index.html) - ElemStatLearn: Data sets, functions and examples from the book: "The Elements of Statistical Learning, Data Mining, Inference, and Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman * [evtree](http://cran.r-project.org/web/packages/evtree/index.html) - evtree: Evolutionary Learning of Globally Optimal Trees +* [forecast](http://cran.r-project.org/web/packages/forecast/index.html) - forecast: Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models +* [forecastHybrid](http://cran.r-project.org/web/packages/forecastHybrid/index.html) - forecastHybrid: Automatic ensemble and cross validation of ARIMA, ETS, STLM, TBATS, and neural network models from the "forecast" package * [fpc](http://cran.r-project.org/web/packages/fpc/index.html) - fpc: Flexible procedures for clustering * [frbs](http://cran.r-project.org/web/packages/frbs/index.html) - frbs: Fuzzy Rule-based Systems for Classification and Regression Tasks * [GAMBoost](http://cran.r-project.org/web/packages/GAMBoost/index.html) - GAMBoost: Generalized linear and additive models by likelihood based boosting @@ -1006,6 +1080,7 @@ on MNIST digits[DEEP LEARNING] * [Optunity](http://docs.optunity.net) - A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search. Optunity is written in Python but interfaces seamlessly to R. * [igraph](http://igraph.org/r/) - binding to igraph library - General purpose graph library * [MXNet](https://github.com/dmlc/mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more. +* [TDSP-Utilities](https://github.com/Azure/Azure-TDSP-Utilities) - Two data science utilities in R from Microsoft: 1) Interactive Data Exploration, Analysis, and Reporting (IDEAR) ; 2) Automated Modeling and Reporting (AMR). #### Data Analysis / Data Visualization @@ -1089,6 +1164,8 @@ on MNIST digits[DEEP LEARNING] * [H2O Sparkling Water](https://github.com/h2oai/sparkling-water) - H2O and Spark interoperability. * [FlinkML in Apache Flink](https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/libs/ml/index.html) - Distributed machine learning library in Flink * [DynaML](https://github.com/mandar2812/DynaML) - Scala Library/REPL for Machine Learning Research +* [Saul](https://github.com/IllinoisCogComp/saul/) - Flexible Declarative Learning-Based Programming. +* [SwiftLearner](https://github.com/valdanylchuk/swiftlearner/) - Simply written algorithms to help study ML or write your own implementations. ## Swift @@ -1103,6 +1180,15 @@ on MNIST digits[DEEP LEARNING] * [DeepLearningKit](http://deeplearningkit.org/) an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS. It currently allows using deep convolutional neural network models trained in Caffe on Apple operating systems. * [AIToolbox](https://github.com/KevinCoble/AIToolbox) - A toolbox framework of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians. +* [MLKit](https://github.com/Somnibyte/MLKit) - A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression. +* [Swift Brain](https://github.com/vlall/Swift-Brain) - The first neural network / machine learning library written in Swift. This is a project for AI algorithms in Swift for iOS and OS X development. This project includes algorithms focused on Bayes theorem, neural networks, SVMs, Matrices, etc.. + + +## TensorFlow + + +#### General-Purpose Machine Learning +* [Awesome TensorFlow](https://github.com/jtoy/awesome-tensorflow) - A list of all things related to TensorFlow ## Credits diff --git a/blogs.md b/blogs.md index d6b84c1..87996d4 100644 --- a/blogs.md +++ b/blogs.md @@ -25,6 +25,8 @@ Podcasts Data Science / Statistics ------------------------- +https://jeremykun.com/ + http://iamtrask.github.io/ http://blog.explainmydata.com/ @@ -91,6 +93,8 @@ http://www.randalolson.com/blog/ http://www.johndcook.com/blog/r_language_for_programmers/ +http://www.dataschool.io/ + Math ---- diff --git a/books.md b/books.md index 5cd6735..38ba772 100644 --- a/books.md +++ b/books.md @@ -2,6 +2,7 @@ The following is a list of free, open source books on machine learning, statisti ## Machine-Learning / Data Mining +* [Real World Machine Learning](https://manning.com/books/real-world-machine-learning) [Free Chapters] * [An Introduction To Statistical Learning](http://www-bcf.usc.edu/~gareth/ISL/) - Book + R Code * [Elements of Statistical Learning](http://statweb.stanford.edu/~tibs/ElemStatLearn/) - Book * [Probabilistic Programming & Bayesian Methods for Hackers](http://camdavidsonpilon.github.io/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers/) - Book + IPython Notebooks @@ -23,9 +24,7 @@ The following is a list of free, open source books on machine learning, statisti * [Reinforcement Learning](http://www.intechopen.com/books/reinforcement_learning) * [Machine Learning](http://www.intechopen.com/books/machine_learning) * [A Quest for AI](http://ai.stanford.edu/~nilsson/QAI/qai.pdf) -* [Introduction to Applied Bayesian -Statistics and Estimation for -Social Scientists](http://faculty.ksu.edu.sa/69424/us_BOOk/Introduction%20to%20Applied%20Bayesian%20Statistics.pdf) +* [Introduction to Applied Bayesian Statistics and Estimation for Social Scientists](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.177.857&rep=rep1&type=pdf) - Scott M. Lynch * [Bayesian Modeling, Inference and Prediction](http://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf) * [A Course in Machine Learning](http://ciml.info/) @@ -52,6 +51,7 @@ and Prediction](http://users.soe.ucsc.edu/~draper/draper-BMIP-dec2005.pdf) ## Neural Networks * [A Brief Introduction to Neural Networks](http://www.dkriesel.com/_media/science/neuronalenetze-en-zeta2-2col-dkrieselcom.pdf) +* [Neural Networks and Deep Learning](http://neuralnetworksanddeeplearning.com/) ## Probability & Statistics