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2025-07-18 22:22:32 +02:00
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 Awesome Machine Learning !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome) !Track Awesome List 
 (https://www.trackawesomelist.com/badge.svg) (https://www.trackawesomelist.com/josephmisiti/awesome-machine-learning/)
 Awesome Machine Learning !Awesome (https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg) (https://github.com/sindresorhus/awesome) !Track Awesome List (https://www.trackawesomelist.com/badge.svg) 
 (https://www.trackawesomelist.com/josephmisiti/awesome-machine-learning/)
A curated list of awesome machine learning frameworks, libraries and software (by language). Inspired by awesome-php.
@@ -42,6 +42,7 @@
 - **Speech Recognition** (#cpp-speech-recognition) 
 - **Sequence Analysis** (#cpp-sequence-analysis) 
 - **Gesture Detection** (#cpp-gesture-detection) 
 - **Reinforcement Learning** (#cpp-reinforcement-learning) 
 - Common Lisp (#common-lisp)
 - **General-Purpose Machine Learning** (#common-lisp-general-purpose-machine-learning)
 - Clojure (#clojure)
@@ -131,6 +132,7 @@
 - **Federated Learning** (#python-federated-learning) 
 - **Kaggle Competition Source Code** (#python-kaggle-competition-source-code) 
 - **Reinforcement Learning** (#python-reinforcement-learning) 
 - **Speech Recognition** (#python-speech-recognition) 
 - Ruby (#ruby)
 - **Natural Language Processing** (#ruby-natural-language-processing) 
 - **General-Purpose Machine Learning** (#ruby-general-purpose-machine-learning) 
@@ -185,8 +187,8 @@
⟡ Recommender (https://github.com/GHamrouni/Recommender) - A C library for product recommendations/suggestions using collaborative filtering (CF).
⟡ Hybrid Recommender System (https://github.com/SeniorSA/hybrid-rs-trainner) - A hybrid recommender system based upon scikit-learn algorithms. Deprecated 
⟡ neonrvm (https://github.com/siavashserver/neonrvm) - neonrvm is an open source machine learning library based on RVM technique. It's written in C programming language and comes with Python programming language bindings.
⟡ cONNXr (https://github.com/alrevuelta/cONNXr) - An ONNX runtime written in pure C (99) with zero dependencies focused on small embedded devices. Run inference on your machine learning models no matter which framework you train it 
with. Easy to install and compiles everywhere, even in very old devices.
⟡ cONNXr (https://github.com/alrevuelta/cONNXr) - An ONNX runtime written in pure C (99) with zero dependencies focused on small embedded devices. Run inference on your machine learning models no matter which framework you train it with. Easy to 
install and compiles everywhere, even in very old devices.
⟡ libonnx (https://github.com/xboot/libonnx) - A lightweight, portable pure C99 onnx inference engine for embedded devices with hardware acceleration support.
@@ -213,28 +215,31 @@
⟡ Speedster (https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/speedster) -Automatically apply SOTA optimization techniques to achieve the maximum inference speed-up on your hardware. DEEP LEARNING 
⟡ BanditLib (https://github.com/jkomiyama/banditlib) - A simple Multi-armed Bandit library. Deprecated 
⟡ Caffe (https://github.com/BVLC/caffe) - A deep learning framework developed with cleanliness, readability, and speed in mind. DEEP LEARNING 
⟡ CatBoost (https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports
CPU and GPU (even multi-GPU) computation.
⟡ CatBoost (https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, contains fast inference implementation and supports CPU and 
GPU (even multi-GPU) computation.
⟡ CNTK (https://github.com/Microsoft/CNTK) - The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.
⟡ CUDA (https://code.google.com/p/cuda-convnet/) - This is a fast C++/CUDA implementation of convolutional DEEP LEARNING 
⟡ DeepDetect (https://github.com/jolibrain/deepdetect) - A machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications.
⟡ Distributed Machine learning Tool Kit (DMTK) (http://www.dmtk.io/) - A distributed machine learning (parameter server) framework by Microsoft. Enables training models on large data sets across multiple machines. Current tools bundled 
with it include: LightLDA and Distributed (Multisense) Word Embedding.
⟡ Distributed Machine learning Tool Kit (DMTK) (http://www.dmtk.io/) - A distributed machine learning (parameter server) framework by Microsoft. Enables training models on large data sets across multiple machines. Current tools bundled with it 
include: LightLDA and Distributed (Multisense) Word Embedding.
⟡ DLib (http://dlib.net/ml.html) - A suite of ML tools designed to be easy to imbed in other applications.
⟡ 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.
⟡ 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.
⟡ Fido (https://github.com/FidoProject/Fido) - A highly-modular C++ machine learning library for embedded electronics and robotics.
⟡ FlexML (https://github.com/ozguraslank/flexml) - Easy-to-use and flexible AutoML library for Python.
⟡ igraph (http://igraph.org/) - General purpose graph library.
⟡ Intel® oneAPI Data Analytics Library (https://github.com/oneapi-src/oneDAL) - 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.
⟡ 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.
⟡ Intel® oneAPI Data Analytics Library (https://github.com/oneapi-src/oneDAL) - 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.
⟡ 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.
⟡ libfm (https://github.com/srendle/libfm) - A generic approach that allows to mimic most factorization models by feature engineering.
⟡ MLDB (https://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.
⟡ mlpack (https://www.mlpack.org/) - A scalable C++ machine learning library.
⟡ MXNet (https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
⟡ N2D2 (https://github.com/CEA-LIST/N2D2) - CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms
⟡ oneDNN (https://github.com/oneapi-src/oneDNN) - An open-source cross-platform performance library for deep learning applications.
⟡ Opik (https://www.comet.com/site/products/opik/) - Open source engineering platform to debug, evaluate, and monitor your LLM applications, RAG systems, and agentic workflows with comprehensive tracing, automated evaluations, and 
production-ready dashboards. (Source Code (https://github.com/comet-ml/opik/))
⟡ ParaMonte (https://github.com/cdslaborg/paramonte) - A general-purpose library with C/C++ interface for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found here 
(https://www.cdslab.org/paramonte/).
⟡ proNet-core (https://github.com/cnclabs/proNet-core) - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
@@ -245,22 +250,22 @@
⟡ Shogun (https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox.
⟡ sofia-ml (https://code.google.com/archive/p/sofia-ml) - Suite of fast incremental algorithms.
⟡ Stan (http://mc-stan.org/) - A probabilistic programming language implementing full Bayesian statistical inference with Hamiltonian Monte Carlo sampling.
⟡ Timbl (https://languagemachines.github.io/timbl/) - A software package/C++ library implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification, and IGTree, a 
decision-tree approximation of IB1-IG. Commonly used for NLP.
⟡ Timbl (https://languagemachines.github.io/timbl/) - A software package/C++ library implementing several memory-based learning algorithms, among which IB1-IG, an implementation of k-nearest neighbor classification, and IGTree, a decision-tree 
approximation of IB1-IG. Commonly used for NLP.
⟡ Vowpal Wabbit (VW) (https://github.com/VowpalWabbit/vowpal_wabbit) - A fast out-of-core learning system.
⟡ Warp-CTC (https://github.com/baidu-research/warp-ctc) - A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.
⟡ XGBoost (https://github.com/dmlc/xgboost) - A parallelized optimized general purpose gradient boosting library.
⟡ ThunderGBM (https://github.com/Xtra-Computing/thundergbm) - A fast library for GBDTs and Random Forests on GPUs.
⟡ ThunderSVM (https://github.com/Xtra-Computing/thundersvm) - A fast SVM library on GPUs and CPUs.
⟡ LKYDeepNN (https://github.com/mosdeo/LKYDeepNN) - A header-only C++11 Neural Network library. Low dependency, native traditional chinese document.
⟡ xLearn (https://github.com/aksnzhy/xlearn) - A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine 
learning problems on large-scale sparse data, which is very common in Internet services such as online advertising and recommender systems.
⟡ Featuretools (https://github.com/featuretools/featuretools) - A library for automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning using reusable 
feature engineering "primitives".
⟡ xLearn (https://github.com/aksnzhy/xlearn) - A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning 
problems on large-scale sparse data, which is very common in Internet services such as online advertising and recommender systems.
⟡ Featuretools (https://github.com/featuretools/featuretools) - A library for automated feature engineering. It excels at transforming transactional and relational datasets into feature matrices for machine learning using reusable feature 
engineering "primitives".
⟡ skynet (https://github.com/Tyill/skynet) - A library for learning neural networks, has C-interface, net set in JSON. Written in C++ with bindings in Python, C++ and C#.
⟡ Feast (https://github.com/gojek/feast) - A feature store for the management, discovery, and access of machine learning features. Feast provides a consistent view of feature data for both model training and model serving.
⟡ Hopsworks (https://github.com/logicalclocks/hopsworks) - A data-intensive platform for AI with the industry's first open-source feature store. The Hopsworks Feature Store provides both a feature warehouse for training and batch based 
on Apache Hive and a feature serving database, based on MySQL Cluster, for online applications.
⟡ Hopsworks (https://github.com/logicalclocks/hopsworks) - A data-intensive platform for AI with the industry's first open-source feature store. The Hopsworks Feature Store provides both a feature warehouse for training and batch based on Apache 
Hive and a feature serving database, based on MySQL Cluster, for online applications.
⟡ Polyaxon (https://github.com/polyaxon/polyaxon) - A platform for reproducible and scalable machine learning and deep learning.
⟡ QuestDB (https://questdb.io/) - A relational column-oriented database designed for real-time analytics on time series and event data.
⟡ Phoenix (https://phoenix.arize.com) - Uncover insights, surface problems, monitor and fine tune your generative LLM, CV and tabular models.
@@ -271,8 +276,7 @@
Natural Language Processing
⟡ BLLIP Parser (https://github.com/BLLIP/bllip-parser) - BLLIP Natural Language Parser (also known as the Charniak-Johnson parser).
⟡ colibri-core
 (https://github.com/proycon/colibri-core) - C++ library, command line tools, and Python binding for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
⟡ colibri-core (https://github.com/proycon/colibri-core) - C++ library, command line tools, and Python binding for extracting and working with basic linguistic constructions such as n-grams and skipgrams in a quick and memory-efficient way.
⟡ CRF++ (https://taku910.github.io/crfpp/) - Open source implementation of Conditional Random Fields (CRFs) for segmenting/labeling sequential data & other Natural Language Processing tasks. Deprecated 
⟡ CRFsuite (http://www.chokkan.org/software/crfsuite/) - CRFsuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. Deprecated 
⟡ frog (https://github.com/LanguageMachines/frog) - Memory-based NLP suite developed for Dutch: PoS tagger, lemmatiser, dependency parser, NER, shallow parser, morphological analyzer.
@@ -294,6 +298,10 @@
⟡ grt (https://github.com/nickgillian/grt) - The Gesture Recognition Toolkit (GRT) is a cross-platform, open-source, C++ machine learning library designed for real-time gesture recognition.
Reinforcement Learning
⟡ RLtools (https://github.com/rl-tools/rl-tools) - The fastest deep reinforcement learning library for continuous control, implemented header-only in pure, dependency-free C++ (Python bindings available as well).
Common Lisp
@@ -350,10 +358,9 @@
Data Visualization
⟡ Hanami (https://github.com/jsa-aerial/hanami) : Clojure(Script) library and framework for creating interactive visualization applications based in Vega-Lite (VGL) and/or Vega (VG) specifications. Automatic framing and layouts along 
with a powerful templating system for abstracting visualization specs
⟡ Saite
 (https://github.com/jsa-aerial/saite) - Clojure(Script) client/server application for dynamic interactive explorations and the creation of live shareable documents capturing them using Vega/Vega-Lite, CodeMirror, markdown, and LaTeX
⟡ Hanami (https://github.com/jsa-aerial/hanami) : Clojure(Script) library and framework for creating interactive visualization applications based in Vega-Lite (VGL) and/or Vega (VG) specifications. Automatic framing and layouts along with a 
powerful templating system for abstracting visualization specs
⟡ Saite (https://github.com/jsa-aerial/saite) - Clojure(Script) client/server application for dynamic interactive explorations and the creation of live shareable documents capturing them using Vega/Vega-Lite, CodeMirror, markdown, and LaTeX
⟡ Oz (https://github.com/metasoarous/oz) - Data visualisation using Vega/Vega-Lite and Hiccup, and a live-reload platform for literate-programming
⟡ Envision (https://github.com/clojurewerkz/envision) - Clojure Data Visualisation library, based on Statistiker and D3.
⟡ Pink Gorilla Notebook (https://github.com/pink-gorilla/gorilla-notebook) - A Clojure/Clojurescript notebook application/-library based on Gorilla-REPL
@@ -539,8 +546,8 @@
⟡ ClearTK (https://github.com/ClearTK/cleartk) - ClearTK provides a framework for developing statistical natural language processing (NLP) components in Java and is built on top of Apache UIMA. Deprecated 
⟡ Apache cTAKES
 (https://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.
⟡ NLP4J (https://github.com/emorynlp/nlp4j) - The NLP4J 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. Deprecated 
⟡ NLP4J (https://github.com/emorynlp/nlp4j) - The NLP4J 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. Deprecated 
⟡ CogcompNLP (https://github.com/CogComp/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.
@@ -553,8 +560,8 @@
⟡ Chips-n-Salsa (https://github.com/cicirello/Chips-n-Salsa) - A Java library for genetic algorithms, evolutionary computation, and stochastic local search, with a focus on self-adaptation / self-tuning, as well as parallel execution.
⟡ Datumbox (https://github.com/datumbox/datumbox-framework) - Machine Learning framework for rapid development of Machine Learning and Statistical applications.
⟡ ELKI (https://elki-project.github.io/) - 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 trainings 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.
⟡ 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 trainings 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.
⟡ FlinkML in Apache Flink (https://ci.apache.org/projects/flink/flink-docs-master/dev/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.
@@ -574,8 +581,8 @@
⟡ SystemML (https://github.com/apache/systemml) - flexible, scalable machine learning (ML) language.
⟡ Tribou (https://tribuo.org) - A machine learning library written in Java by Oracle.
⟡ Weka (https://www.cs.waikato.ac.nz/ml/weka/) - Weka is a collection of machine learning algorithms for data mining tasks.
⟡ LBJava (https://github.com/CogComp/lbjava) - Learning Based Java is a modelling 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.
⟡ LBJava (https://github.com/CogComp/lbjava) - Learning Based Java is a modelling 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.
⟡ knn-java-library (https://github.com/felipexw/knn-java-library) - Just a simple implementation of K-Nearest Neighbors algorithm using with a bunch of similarity measures.
@@ -648,6 +655,7 @@
⟡ Auto ML (https://github.com/ClimbsRocks/auto_ml) - Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a .csv file! Deprecated 
⟡ Convnet.js (https://cs.stanford.edu/people/karpathy/convnetjs/) - ConvNetJS is a JavaScript library for training Deep Learning modelsDEEP LEARNING Deprecated 
⟡ Creatify MCP (https://github.com/TSavo/creatify-mcp) - Model Context Protocol server that exposes Creatify AI's video generation capabilities to AI assistants, enabling natural language video creation workflows.
⟡ Clusterfck (https://harthur.github.io/clusterfck/) - Agglomerative hierarchical clustering implemented in JavaScript for Node.js and the browser. Deprecated 
⟡ Clustering.js (https://github.com/emilbayes/clustering.js) - Clustering algorithms implemented in JavaScript for Node.js and the browser. Deprecated 
⟡ Decision Trees (https://github.com/serendipious/nodejs-decision-tree-id3) - NodeJS Implementation of Decision Tree using ID3 Algorithm. Deprecated 
@@ -680,20 +688,21 @@
⟡ Netron (https://github.com/lutzroeder/netron) - Visualizer for machine learning models.
⟡ tensor-js (https://github.com/Hoff97/tensorjs) - A deep learning library for the browser, accelerated by WebGL and WebAssembly.
⟡ WebDNN (https://github.com/mil-tokyo/webdnn) - Fast Deep Neural Network JavaScript Framework. WebDNN uses next generation JavaScript API, WebGPU for GPU execution, and WebAssembly for CPU execution.
⟡ WebNN (https://webnn.dev) - A new web standard that allows web apps and frameworks to accelerate deep neural networks with on-device hardware such as GPUs, CPUs, or purpose-built AI accelerators.
Misc
⟡ stdlib (https://github.com/stdlib-js/stdlib) - A standard library for JavaScript and Node.js, with an emphasis on numeric computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, 
streams, utilities, and more.
⟡ stdlib (https://github.com/stdlib-js/stdlib) - A standard library for JavaScript and Node.js, with an emphasis on numeric computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, 
utilities, and more.
⟡ sylvester (https://github.com/jcoglan/sylvester) - Vector and Matrix math for JavaScript. Deprecated 
⟡ simple-statistics (https://github.com/simple-statistics/simple-statistics) - A JavaScript implementation of descriptive, regression, and inference statistics. Implemented in literate JavaScript with no dependencies, designed to work 
in all modern browsers (including IE) as well as in Node.js.
⟡ simple-statistics (https://github.com/simple-statistics/simple-statistics) - A JavaScript implementation of descriptive, regression, and inference statistics. Implemented in literate JavaScript with no dependencies, designed to work in all 
modern browsers (including IE) as well as in Node.js.
⟡ regression-js (https://github.com/Tom-Alexander/regression-js) - A javascript library containing a collection of least squares fitting methods for finding a trend in a set of data.
⟡ Lyric (https://github.com/flurry/Lyric) - Linear Regression library. Deprecated 
⟡ GreatCircle (https://github.com/mwgg/GreatCircle) - Library for calculating great circle distance.
⟡ MLPleaseHelp (https://github.com/jgreenemi/MLPleaseHelp) - MLPleaseHelp is a simple ML resource search engine. You can use this search engine right now at https://jgreenemi.github.io/MLPleaseHelp/ 
(https://jgreenemi.github.io/MLPleaseHelp/), provided via GitHub Pages.
⟡ MLPleaseHelp (https://github.com/jgreenemi/MLPleaseHelp) - MLPleaseHelp is a simple ML resource search engine. You can use this search engine right now at https://jgreenemi.github.io/MLPleaseHelp/ (https://jgreenemi.github.io/MLPleaseHelp/), 
provided via GitHub Pages.
⟡ Pipcook (https://github.com/alibaba/pipcook) - A JavaScript application framework for machine learning and its engineering.
@@ -745,6 +754,7 @@
⟡ Knet (https://github.com/denizyuret/Knet.jl) - Koç University Deep Learning Framework.
⟡ Flux (https://fluxml.ai/) - Relax! Flux is the ML library that doesn't make you tensor
⟡ MLJ (https://github.com/alan-turing-institute/MLJ.jl) - A Julia machine learning framework.
⟡ CluGen (https://github.com/clugen/CluGen.jl/) - Multidimensional cluster generation in Julia.
Natural Language Processing
@@ -798,8 +808,8 @@
General-Purpose Machine Learning
⟡ Torch7 (http://torch.ch/)
  ⟡ cephes (https://github.com/deepmind/torch-cephes) - Cephes mathematical functions library, wrapped for Torch. Provides and wraps the 180+ special mathematical functions from the Cephes mathematical library, developed by Stephen L. 
Moshier. It is used, among many other places, at the heart of SciPy. Deprecated 
  ⟡ cephes (https://github.com/deepmind/torch-cephes) - Cephes mathematical functions library, wrapped for Torch. Provides and wraps the 180+ special mathematical functions from the Cephes mathematical library, developed by Stephen L. Moshier. It
is used, among many other places, at the heart of SciPy. Deprecated 
  ⟡ autograd (https://github.com/twitter/torch-autograd) - Autograd automatically differentiates native Torch code. Inspired by the original Python version.
  ⟡ graph (https://github.com/torch/graph) - Graph package for Torch. Deprecated 
  ⟡ randomkit (https://github.com/deepmind/torch-randomkit) - Numpy's randomkit, wrapped for Torch. Deprecated 
@@ -810,25 +820,22 @@
  ⟡ 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.
  ⟡ dpnn (https://github.com/Element-Research/dpnn) - Many useful features that aren't part of the main nn package.
  ⟡ dp (https://github.com/nicholas-leonard/dp) - A deep learning library designed for streamlining research and development using the Torch7 distribution. It emphasizes flexibility through the elegant use of object-oriented design 
patterns. Deprecated 
  ⟡ dp (https://github.com/nicholas-leonard/dp) - A deep learning library designed for streamlining research and development using the Torch7 distribution. It emphasizes flexibility through the elegant use of object-oriented design patterns. 
Deprecated 
  ⟡ optim (https://github.com/torch/optim) - An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more.
  ⟡ unsup (https://github.com/koraykv/unsup) - A package for unsupervised learning in Torch. Provides modules that are compatible with nn (LinearPsd, ConvPsd, AutoEncoder, ...), and self-contained algorithms (k-means, PCA). Deprecated 
  ⟡ manifold (https://github.com/clementfarabet/manifold) - A package to manipulate manifolds.
  ⟡ svm (https://github.com/koraykv/torch-svm) - Torch-SVM library. Deprecated 
  ⟡ lbfgs (https://github.com/clementfarabet/lbfgs) - FFI Wrapper for liblbfgs. Deprecated 
  ⟡ vowpalwabbit (https://github.com/clementfarabet/vowpal_wabbit) - An old vowpalwabbit interface to torch. Deprecated 
  ⟡ OpenGM (https://github.com/clementfarabet/lua---opengm) - OpenGM is a C++ library for graphical modelling, and inference. The Lua bindings provide a simple way of describing graphs, from Lua, and then optimizing them with OpenGM. 
Deprecated 
  ⟡ OpenGM (https://github.com/clementfarabet/lua---opengm) - OpenGM is a C++ library for graphical modelling, and inference. The Lua bindings provide a simple way of describing graphs, from Lua, and then optimizing them with OpenGM. Deprecated 
  ⟡ spaghetti (https://github.com/MichaelMathieu/lua---spaghetti) - Spaghetti (sparse linear) module for torch7 by @MichaelMathieu Deprecated 
  ⟡ LuaSHKit (https://github.com/ocallaco/LuaSHkit) - A Lua wrapper around the Locality sensitive hashing library SHKit Deprecated 
  ⟡ kernel smoothing (https://github.com/rlowrance/kernel-smoothers) - KNN, kernel-weighted average, local linear regression smoothers. Deprecated 
  ⟡ cutorch (https://github.com/torch/cutorch) - Torch CUDA Implementation.
  ⟡ cunn (https://github.com/torch/cunn) - Torch CUDA Neural Network Implementation.
  ⟡ imgraph (https://github.com/clementfarabet/lua---imgraph) - An image/graph library for Torch. This package provides routines to construct graphs on images, segment them, build trees out of them, and convert them back to images. 
Deprecated 
  ⟡ videograph (https://github.com/clementfarabet/videograph) - A video/graph library for Torch. This package provides routines to construct graphs on videos, segment them, build trees out of them, and convert them back to videos. 
Deprecated 
  ⟡ imgraph (https://github.com/clementfarabet/lua---imgraph) - An image/graph library for Torch. This package provides routines to construct graphs on images, segment them, build trees out of them, and convert them back to images. Deprecated 
  ⟡ videograph (https://github.com/clementfarabet/videograph) - A video/graph library for Torch. This package provides routines to construct graphs on videos, segment them, build trees out of them, and convert them back to videos. Deprecated 
  ⟡ saliency (https://github.com/marcoscoffier/torch-saliency) - code and tools around integral images. A library for finding interest points based on fast integral histograms. Deprecated 
  ⟡ stitch (https://github.com/marcoscoffier/lua---stitch) - allows us to use hugin to stitch images and apply same stitching to a video sequence. Deprecated 
  ⟡ sfm (https://github.com/marcoscoffier/lua---sfm) - A bundle adjustment/structure from motion package. Deprecated 
@@ -892,8 +899,8 @@
⟡ Training a deep autoencoder or a classifier
on MNIST digits (https://www.cs.toronto.edu/~hinton/MatlabForSciencePaper.html) - Training a deep autoencoder or a classifier
on MNIST digitsDEEP LEARNING .
⟡ Convolutional-Recursive Deep Learning for 3D Object Classification (https://www.socher.org/index.php/Main/Convolutional-RecursiveDeepLearningFor3DObjectClassification) - Convolutional-Recursive Deep Learning for 3D Object 
ClassificationDEEP LEARNING .
⟡ Convolutional-Recursive Deep Learning for 3D Object Classification (https://www.socher.org/index.php/Main/Convolutional-RecursiveDeepLearningFor3DObjectClassification) - Convolutional-Recursive Deep Learning for 3D Object ClassificationDEEP 
LEARNING .
⟡ Spider (https://people.kyb.tuebingen.mpg.de/spider/) - The spider is intended to be a complete object orientated environment for machine learning in Matlab.
⟡ LibSVM (https://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab) - A Library for Support Vector Machines.
⟡ ThunderSVM (https://github.com/Xtra-Computing/thundersvm) - An Open-Source SVM Library on GPUs and CPUs
@@ -902,12 +909,12 @@
⟡ Caffe (https://github.com/BVLC/caffe) - A deep learning framework developed with cleanliness, readability, and speed in mind.
⟡ Pattern Recognition Toolbox (https://github.com/covartech/PRT) - A complete object-oriented environment for machine learning in Matlab.
⟡ Pattern Recognition and Machine Learning (https://github.com/PRML/PRMLT) - This package contains the matlab implementation of the algorithms described in the book Pattern Recognition and Machine Learning by C. Bishop.
⟡ Optunity (https://optunity.readthedocs.io/en/latest/) - 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 with MATLAB.
⟡ Optunity (https://optunity.readthedocs.io/en/latest/) - 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 with MATLAB.
⟡ MXNet (https://github.com/apache/incubator-mxnet/) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, JavaScript and more.
⟡ Machine Learning in MatLab/Octave
 (https://github.com/trekhleb/machine-learning-octave) - Examples of popular machine learning algorithms (neural networks, linear/logistic regressions, K-Means, etc.) with code examples and mathematics behind them being explained.
⟡ MOCluGen (https://github.com/clugen/MOCluGen/) - Multidimensional cluster generation in MATLAB/Octave.
Data Analysis / Data Visualization
@@ -926,8 +933,7 @@
⟡ OpenCVDotNet (https://code.google.com/archive/p/opencvdotnet) - A wrapper for the OpenCV project to be used with .NET applications.
⟡ Emgu CV (http://www.emgu.com/wiki/index.php/Main_Page) - Cross platform wrapper of OpenCV which can be compiled in Mono to be run on Windows, Linus, Mac OS X, iOS, and Android.
⟡ AForge.NET (http://www.aforgenet.com/framework/) - Open source C# framework for developers and researchers in the fields of Computer Vision and Artificial Intelligence. Development has now shifted to GitHub.
⟡ Accord.NET
 (http://accord-framework.net) - Together with AForge.NET, this library can provide image processing and computer vision algorithms to Windows, Windows RT and Windows Phone. Some components are also available for Java and Android.
⟡ Accord.NET (http://accord-framework.net) - Together with AForge.NET, this library can provide image processing and computer vision algorithms to Windows, Windows RT and Windows Phone. Some components are also available for Java and Android.
Natural Language Processing
@@ -938,23 +944,21 @@
General-Purpose Machine Learning
⟡ Accord-Framework (http://accord-framework.net/) -The Accord.NET Framework is a complete framework for building machine learning, computer vision, computer audition, signal processing and statistical applications.
⟡ Accord.MachineLearning (https://www.nuget.org/packages/Accord.MachineLearning/) - Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation
and Grid-Search for machine-learning applications. This package is part of the Accord.NET Framework.
⟡ DiffSharp (https://diffsharp.github.io/DiffSharp/) - An automatic differentiation (AD) library providing exact and efficient derivatives (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and 
Jacobian-vector products) for machine learning and optimization applications. Operations can be nested to any level, meaning that you can compute exact higher-order derivatives and differentiate functions that are internally making use 
of differentiation, for applications such as hyperparameter optimization.
⟡ Encog (https://www.nuget.org/packages/encog-dotnet-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.
⟡ GeneticSharp (https://github.com/giacomelli/GeneticSharp) - Multi-platform genetic algorithm library for .NET Core and .NET Framework. The library has several implementations of GA operators, like: selection, crossover, mutation, 
reinsertion and termination.
⟡ Accord.MachineLearning (https://www.nuget.org/packages/Accord.MachineLearning/) - Support Vector Machines, Decision Trees, Naive Bayesian models, K-means, Gaussian Mixture models and general algorithms such as Ransac, Cross-validation and 
Grid-Search for machine-learning applications. This package is part of the Accord.NET Framework.
⟡ DiffSharp (https://diffsharp.github.io/DiffSharp/) - An automatic differentiation (AD) library providing exact and efficient derivatives (gradients, Hessians, Jacobians, directional derivatives, and matrix-free Hessian- and Jacobian-vector 
products) for machine learning and optimization applications. Operations can be nested to any level, meaning that you can compute exact higher-order derivatives and differentiate functions that are internally making use of differentiation, for 
applications such as hyperparameter optimization.
⟡ Encog (https://www.nuget.org/packages/encog-dotnet-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.
⟡ GeneticSharp
 (https://github.com/giacomelli/GeneticSharp) - Multi-platform genetic algorithm library for .NET Core and .NET Framework. The library has several implementations of GA operators, like: selection, crossover, mutation, reinsertion and termination.
⟡ Infer.NET (https://dotnet.github.io/infer/) - Infer.NET is a framework for running Bayesian inference in graphical models. One can use Infer.NET to solve many different kinds of machine learning problems, from standard problems like 
classification, recommendation or clustering through customized solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many 
others.
⟡ ML.NET (https://github.com/dotnet/machinelearning) - ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers. ML.NET was originally developed in Microsoft Research 
and evolved into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.
⟡ Neural Network Designer (https://sourceforge.net/projects/nnd/) - DBMS management system and designer for neural networks. The designer application is developed using WPF, and is a user interface which allows you to design your neural
network, query the network, create and configure chat bots that are capable of asking questions and learning from your feedback. The chat bots can even scrape the internet for information to return in their output as well as to use for 
learning.
classification, recommendation or clustering through customized solutions to domain-specific problems. Infer.NET has been used in a wide variety of domains including information retrieval, bioinformatics, epidemiology, vision, and many others.
⟡ ML.NET (https://github.com/dotnet/machinelearning) - ML.NET is a cross-platform open-source machine learning framework which makes machine learning accessible to .NET developers. ML.NET was originally developed in Microsoft Research and evolved
into a significant framework over the last decade and is used across many product groups in Microsoft like Windows, Bing, PowerPoint, Excel and more.
⟡ Neural Network Designer (https://sourceforge.net/projects/nnd/) - DBMS management system and designer for neural networks. The designer application is developed using WPF, and is a user interface which allows you to design your neural network, 
query the network, create and configure chat bots that are capable of asking questions and learning from your feedback. The chat bots can even scrape the internet for information to return in their output as well as to use for learning.
⟡ Synapses (https://github.com/mrdimosthenis/Synapses) - Neural network library in F#.
⟡ Vulpes (https://github.com/fsprojects/Vulpes) - Deep belief and deep learning implementation written in F# and leverages CUDA GPU execution with Alea.cuBase.
⟡ MxNet.Sharp
@@ -964,11 +968,11 @@
Data Analysis / Data Visualization
⟡ numl (https://www.nuget.org/packages/numl/) - numl is a machine learning library intended to ease the use of using standard modelling techniques for both prediction and clustering.
⟡ Math.NET Numerics (https://www.nuget.org/packages/MathNet.Numerics/) - Numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and everyday use. 
Supports .Net 4.0, .Net 3.5 and Mono on Windows, Linux and Mac; Silverlight 5, WindowsPhone/SL 8, WindowsPhone 8.1 and Windows 8 with PCL Portable Profiles 47 and 344; Android/iOS with Xamarin.
⟡ Sho (https://www.microsoft.com/en-us/research/project/sho-the-net-playground-for-data/) - Sho is an interactive environment for data analysis and scientific computing that lets you seamlessly connect scripts (in IronPython) with 
compiled code (in .NET) to enable fast and flexible prototyping. The environment includes powerful and efficient libraries for linear algebra as well as data visualization that can be used from any .NET language, as well as a 
feature-rich interactive shell for rapid development.
⟡ Math.NET Numerics (https://www.nuget.org/packages/MathNet.Numerics/) - Numerical foundation of the Math.NET project, aiming to provide methods and algorithms for numerical computations in science, engineering and everyday use. Supports .Net 
4.0, .Net 3.5 and Mono on Windows, Linux and Mac; Silverlight 5, WindowsPhone/SL 8, WindowsPhone 8.1 and Windows 8 with PCL Portable Profiles 47 and 344; Android/iOS with Xamarin.
⟡ Sho (https://www.microsoft.com/en-us/research/project/sho-the-net-playground-for-data/) - Sho is an interactive environment for data analysis and scientific computing that lets you seamlessly connect scripts (in IronPython) with compiled code 
(in .NET) to enable fast and flexible prototyping. The environment includes powerful and efficient libraries for linear algebra as well as data visualization that can be used from any .NET language, as well as a feature-rich interactive shell for
rapid development.
Objective C
@@ -977,18 +981,18 @@
General-Purpose Machine Learning
⟡ YCML (https://github.com/yconst/YCML) - A Machine Learning framework for Objective-C and Swift (OS X / iOS).
⟡ MLPNeuralNet (https://github.com/nikolaypavlov/MLPNeuralNet) - Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural networks. It is built on top of the Apple's 
Accelerate Framework, using vectorized operations and hardware acceleration if available. Deprecated 
⟡ MAChineLearning (https://github.com/gianlucabertani/MAChineLearning) - An Objective-C multilayer perceptron library, with full support for training through backpropagation. Implemented using vDSP and vecLib, it's 20 times faster than 
its Java equivalent. Includes sample code for use from Swift.
⟡ BPN-NeuralNetwork (https://github.com/Kalvar/ios-BPN-NeuralNetwork) - It implemented 3 layers of neural networks ( Input Layer, Hidden Layer and Output Layer ) and it was named Back Propagation Neural Networks (BPN). This network can 
be used in products recommendation, user behavior analysis, data mining and data analysis. Deprecated 
⟡ MLPNeuralNet (https://github.com/nikolaypavlov/MLPNeuralNet) - Fast multilayer perceptron neural network library for iOS and Mac OS X. MLPNeuralNet predicts new examples by trained neural networks. It is built on top of the Apple's Accelerate 
Framework, using vectorized operations and hardware acceleration if available. Deprecated 
⟡ MAChineLearning (https://github.com/gianlucabertani/MAChineLearning) - An Objective-C multilayer perceptron library, with full support for training through backpropagation. Implemented using vDSP and vecLib, it's 20 times faster than its Java 
equivalent. Includes sample code for use from Swift.
⟡ BPN-NeuralNetwork (https://github.com/Kalvar/ios-BPN-NeuralNetwork) - It implemented 3 layers of neural networks ( Input Layer, Hidden Layer and Output Layer ) and it was named Back Propagation Neural Networks (BPN). This network can be used in
products recommendation, user behavior analysis, data mining and data analysis. Deprecated 
⟡ Multi-Perceptron-NeuralNetwork
 (https://github.com/Kalvar/ios-Multi-Perceptron-NeuralNetwork) - It implemented multi-perceptrons neural network (ニューラルネットワーク) based on Back Propagation Neural Networks (BPN) and designed unlimited-hidden-layers.
⟡ KRHebbian-Algorithm (https://github.com/Kalvar/ios-KRHebbian-Algorithm) - It is a non-supervisory and self-learning algorithm (adjust the weights) in the neural network of Machine Learning. Deprecated 
⟡ KRKmeans-Algorithm (https://github.com/Kalvar/ios-KRKmeans-Algorithm) - It implemented K-Means clustering and classification algorithm. It could be used in data mining and image compression. Deprecated 
⟡ KRFuzzyCMeans-Algorithm (https://github.com/Kalvar/ios-KRFuzzyCMeans-Algorithm) - It implemented Fuzzy C-Means (FCM) the fuzzy clustering / classification algorithm on Machine Learning. It could be used in data mining and image 
compression. Deprecated 
⟡ KRFuzzyCMeans-Algorithm (https://github.com/Kalvar/ios-KRFuzzyCMeans-Algorithm) - It implemented Fuzzy C-Means (FCM) the fuzzy clustering / classification algorithm on Machine Learning. It could be used in data mining and image compression. 
Deprecated 
OCaml
@@ -1071,28 +1075,28 @@
Computer Vision
⟡ LightlyTrain (https://github.com/lightly-ai/lightly-train) - Pretrain computer vision models on unlabeled data for industrial applications
⟡ Scikit-Image (https://github.com/scikit-image/scikit-image) - A collection of algorithms for image processing in Python.
⟡ Scikit-Opt
 (https://github.com/guofei9987/scikit-opt) - Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm, Artificial Fish Swarm Algorithm in Python)
⟡ Scikit-Opt (https://github.com/guofei9987/scikit-opt) - Swarm Intelligence in Python (Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing, Ant Colony Algorithm, Immune Algorithm, Artificial Fish Swarm Algorithm in Python)
⟡ 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. Deprecated 
⟡ face_recognition (https://github.com/ageitgey/face_recognition) - Face recognition library that recognizes and manipulates faces from Python or from the command line.
⟡ deepface (https://github.com/serengil/deepface) - A lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for Python covering cutting-edge models such as VGG-Face, FaceNet, OpenFace, 
DeepFace, DeepID, Dlib and ArcFace.
⟡ deepface (https://github.com/serengil/deepface) - A lightweight face recognition and facial attribute analysis (age, gender, emotion and race) framework for Python covering cutting-edge models such as VGG-Face, FaceNet, OpenFace, DeepFace, 
DeepID, Dlib and ArcFace.
⟡ retinaface (https://github.com/serengil/retinaface) - deep learning based cutting-edge facial detector for Python coming with facial landmarks
⟡ dockerface (https://github.com/natanielruiz/dockerface) - Easy to install and use deep learning Faster R-CNN face detection for images and video in a docker container. Deprecated 
⟡ Detectron (https://github.com/facebookresearch/Detectron) - FAIR's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning 
framework. Deprecated 
⟡ detectron2 (https://github.com/facebookresearch/detectron2) - FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch 
deep learning framework.
⟡ albumentations (https://github.com/albu/albumentations) - А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the 
box. Was used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
⟡ pytessarct (https://github.com/madmaze/pytesseract) - Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded in images. Python-tesseract is a wrapper for 
Google's Tesseract-OCR Engine (https://github.com/tesseract-ocr/tesseract).
⟡ imutils (https://github.com/jrosebr1/imutils) - A library containing Convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier 
with OpenCV and Python.
⟡ Detectron (https://github.com/facebookresearch/Detectron) - FAIR's software system that implements state-of-the-art object detection algorithms, including Mask R-CNN. It is written in Python and powered by the Caffe2 deep learning framework. 
Deprecated 
⟡ detectron2 (https://github.com/facebookresearch/detectron2) - FAIR's next-generation research platform for object detection and segmentation. It is a ground-up rewrite of the previous version, Detectron, and is powered by the PyTorch deep 
learning framework.
⟡ albumentations (https://github.com/albu/albumentations) - А fast and framework agnostic image augmentation library that implements a diverse set of augmentation techniques. Supports classification, segmentation, detection out of the box. Was 
used to win a number of Deep Learning competitions at Kaggle, Topcoder and those that were a part of the CVPR workshops.
⟡ pytessarct (https://github.com/madmaze/pytesseract) - Python-tesseract is an optical character recognition (OCR) tool for python. That is, it will recognize and "read" the text embedded in images. Python-tesseract is a wrapper for Google's 
Tesseract-OCR Engine (https://github.com/tesseract-ocr/tesseract).
⟡ imutils (https://github.com/jrosebr1/imutils) - A library containing Convenience functions to make basic image processing operations such as translation, rotation, resizing, skeletonization, and displaying Matplotlib images easier with OpenCV 
and Python.
⟡ PyTorchCV (https://github.com/donnyyou/PyTorchCV) - A PyTorch-Based Framework for Deep Learning in Computer Vision.
⟡ joliGEN (https://github.com/jolibrain/joliGEN) - Generative AI Image Toolset with GANs and Diffusion for Real-World Applications.
⟡ Self-supervised learning (https://pytorch-lightning-bolts.readthedocs.io/en/latest/self_supervised_models.html)
@@ -1108,18 +1112,16 @@
⟡ Learnergy (https://github.com/gugarosa/learnergy) - Energy-based machine learning models built upon PyTorch.
⟡ OpenVisionAPI (https://github.com/openvisionapi) - Open source computer vision API based on open source models.
⟡ IoT Owl (https://github.com/Ret2Me/IoT-Owl) - Light face detection and recognition system with huge possibilities, based on Microsoft Face API and TensorFlow made for small IoT devices like raspberry pi.
⟡ Exadel CompreFace (https://github.com/exadel-inc/CompreFace) - face recognition system that can be easily integrated into any system without prior machine learning skills. CompreFace provides REST API for face recognition, face 
verification, face detection, face mask detection, landmark detection, age, and gender recognition and is easily deployed with docker.
⟡ computer-vision-in-action (https://github.com/Charmve/computer-vision-in-action) - as known as L0CV, is a new generation of computer vision open source online learning media, a cross-platform interactive learning framework integrating
graphics, source code and HTML. the L0CV ecosystem — Notebook, Datasets, Source Code, and from Diving-in to Advanced — as well as the L0CV Hub.
⟡ timm (https://github.com/rwightman/pytorch-image-models) - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, 
and more.
⟡ Exadel CompreFace (https://github.com/exadel-inc/CompreFace) - face recognition system that can be easily integrated into any system without prior machine learning skills. CompreFace provides REST API for face recognition, face verification, 
face detection, face mask detection, landmark detection, age, and gender recognition and is easily deployed with docker.
⟡ computer-vision-in-action (https://github.com/Charmve/computer-vision-in-action) - as known as L0CV, is a new generation of computer vision open source online learning media, a cross-platform interactive learning framework integrating graphics,
source code and HTML. the L0CV ecosystem — Notebook, Datasets, Source Code, and from Diving-in to Advanced — as well as the L0CV Hub.
⟡ timm (https://github.com/rwightman/pytorch-image-models) - PyTorch image models, scripts, pretrained weights -- ResNet, ResNeXT, EfficientNet, EfficientNetV2, NFNet, Vision Transformer, MixNet, MobileNet-V3/V2, RegNet, DPN, CSPNet, and more.
⟡ segmentation_models.pytorch (https://github.com/qubvel/segmentation_models.pytorch) - A PyTorch-based toolkit that offers pre-trained segmentation models for computer vision tasks. It simplifies the development of image segmentation 
applications by providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object 
segmentation in images.
⟡ segmentation_models (https://github.com/qubvel/segmentation_models) - A TensorFlow Keras-based toolkit that offers pre-trained segmentation models for computer vision tasks. It simplifies the development of image segmentation 
applications by providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object 
segmentation in images.
applications by providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object segmentation in 
images.
⟡ segmentation_models (https://github.com/qubvel/segmentation_models) - A TensorFlow Keras-based toolkit that offers pre-trained segmentation models for computer vision tasks. It simplifies the development of image segmentation applications by 
providing a collection of popular architecture implementations, such as UNet and PSPNet, along with pre-trained weights, making it easier for researchers and developers to achieve high-quality pixel-level object segmentation in images.
⟡ MLX (https://github.com/ml-explore/mlx)- MLX is an array framework for machine learning on Apple silicon, developed by Apple machine learning research.
@@ -1142,8 +1144,8 @@
⟡ BLLIP Parser (https://pypi.org/project/bllipparser/) - Python bindings for the BLLIP Natural Language Parser (also known as the Charniak-Johnson parser). Deprecated 
⟡ PyNLPl (https://github.com/proycon/pynlpl) - Python Natural Language Processing Library. General purpose NLP library for Python. Also contains some specific modules for parsing common NLP formats, most notably for FoLiA 
(https://proycon.github.io/folia/), but also ARPA language models, Moses phrasetables, GIZA++ alignments.
⟡ PySS3 (https://github.com/sergioburdisso/pyss3) - Python package that implements a novel white-box machine learning model for text classification, called SS3. Since SS3 has the ability to visually explain its rationale, this package 
also comes with easy-to-use interactive visualizations tools (online demos (http://tworld.io/ss3/)).
⟡ PySS3 (https://github.com/sergioburdisso/pyss3) - Python package that implements a novel white-box machine learning model for text classification, called SS3. Since SS3 has the ability to visually explain its rationale, this package also comes 
with easy-to-use interactive visualizations tools (online demos (http://tworld.io/ss3/)).
⟡ python-ucto (https://github.com/proycon/python-ucto) - Python binding to ucto (a unicode-aware rule-based tokenizer for various languages).
⟡ python-frog (https://github.com/proycon/python-frog) - Python binding to Frog, an NLP suite for Dutch. (pos tagging, lemmatisation, dependency parsing, NER)
⟡ python-zpar (https://github.com/EducationalTestingService/python-zpar) - Python bindings for ZPar (https://github.com/frcchang/zpar), a statistical part-of-speech-tagger, constituency parser, and dependency parser for English.
@@ -1159,7 +1161,7 @@
⟡ stanford-corenlp-python (https://github.com/dasmith/stanford-corenlp-python) - Python wrapper for Stanford CoreNLP (https://github.com/stanfordnlp/CoreNLP) Deprecated 
⟡ CLTK (https://github.com/cltk/cltk) - The Classical Language Toolkit.
⟡ Rasa (https://github.com/RasaHQ/rasa) - A "machine learning framework to automate text-and voice-based conversations."
⟡ yase (https://github.com/PPACI/yase) - Transcode sentence (or other sequence) to list of word vector .
⟡ yase (https://github.com/PPACI/yase) - Transcode sentence (or other sequence) to list of word vector.
⟡ Polyglot (https://github.com/aboSamoor/polyglot) - Multilingual text (NLP) processing toolkit.
⟡ DrQA (https://github.com/facebookresearch/DrQA) - Reading Wikipedia to answer open-domain questions.
⟡ Dedupe (https://github.com/dedupeio/dedupe) - A python library for accurate and scalable fuzzy matching, record deduplication and entity-resolution.
@@ -1172,6 +1174,7 @@
⟡ Haystack (https://github.com/deepset-ai/haystack) - A framework for building industrial-strength applications with Transformer models and LLMs.
⟡ CometLLM (https://github.com/comet-ml/comet-llm) - Track, log, visualize and evaluate your LLM prompts and prompt chains.
⟡ Transformers (https://github.com/huggingface/transformers) - A deep learning library containing thousands of pre-trained models on different tasks. The goto place for anything related to Large Language Models.
⟡ TextCL (https://github.com/alinapetukhova/textcl) - Text preprocessing package for use in NLP tasks.
General-Purpose Machine Learning
@@ -1190,24 +1193,24 @@
 ⟡ PyTorch Geometric Temporal (https://github.com/benedekrozemberczki/pytorch_geometric_temporal) -> A temporal extension of PyTorch Geometric for dynamic graph representation learning.
 ⟡ Little Ball of Fur (https://github.com/benedekrozemberczki/littleballoffur) -> A graph sampling extension library for NetworkX with a Scikit-Learn like API.
 ⟡ Karate Club (https://github.com/benedekrozemberczki/karateclub) -> An unsupervised machine learning extension library for NetworkX with a Scikit-Learn like API.
⟡ Auto_ViML (https://github.com/AutoViML/Auto_ViML) -> Automatically Build Variant Interpretable ML models fast! Auto_ViML is pronounced "auto vimal", is a comprehensive and scalable Python AutoML toolkit with imbalanced handling, 
ensembling, stacking and built-in feature selection. Featured in .
⟡ PyOD (https://github.com/yzhao062/pyod) -> Python Outlier Detection, comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. Featured for Advanced models, including Neural Networks/Deep Learning 
and Outlier Ensembles.
⟡ Auto_ViML (https://github.com/AutoViML/Auto_ViML) -> Automatically Build Variant Interpretable ML models fast! Auto_ViML is pronounced "auto vimal", is a comprehensive and scalable Python AutoML toolkit with imbalanced handling, ensembling, 
stacking and built-in feature selection. Featured in .
⟡ PyOD (https://github.com/yzhao062/pyod) -> Python Outlier Detection, comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. Featured for Advanced models, including Neural Networks/Deep Learning and 
Outlier Ensembles.
⟡ steppy (https://github.com/neptune-ml/steppy) -> Lightweight, Python library for fast and reproducible machine learning experimentation. Introduces a very simple interface that enables clean machine learning pipeline design.
⟡ steppy-toolkit (https://github.com/neptune-ml/steppy-toolkit) -> Curated collection of the neural networks, transformers and models that make your machine learning work faster and more effective.
⟡ CNTK (https://github.com/Microsoft/CNTK) - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit. Documentation can be found here (https://docs.microsoft.com/cognitive-toolkit/).
⟡ Couler (https://github.com/couler-proj/couler) - Unified interface for constructing and managing machine learning workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
⟡ auto_ml (https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and 
results. Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.
⟡ auto_ml (https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. 
Includes support for NLP, XGBoost, CatBoost, LightGBM, and soon, deep learning.
⟡ dtaidistance (https://github.com/wannesm/dtaidistance) - High performance library for time series distances (DTW) and time series clustering.
⟡ einops (https://github.com/arogozhnikov/einops) - Deep learning operations reinvented (for pytorch, tensorflow, jax and others).
⟡ machine learning (https://github.com/jeff1evesque/machine-learning) - automated build consisting of a web-interface (https://github.com/jeff1evesque/machine-learning#web-interface), and set of programmatic-interface 
(https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL
(https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL 
datastore.
⟡ XGBoost (https://github.com/dmlc/xgboost) - Python bindings for eXtreme Gradient Boosting (Tree) Library.
⟡ ChefBoost (https://github.com/serengil/chefboost) - a lightweight decision tree framework for Python with categorical feature support covering regular decision tree algorithms such as ID3, C4.5, CART, CHAID and regression tree; also 
some advanved bagging and boosting techniques such as gradient boosting, random forest and adaboost.
⟡ ChefBoost (https://github.com/serengil/chefboost) - a lightweight decision tree framework for Python with categorical feature support covering regular decision tree algorithms such as ID3, C4.5, CART, CHAID and regression tree; also some 
advanced bagging and boosting techniques such as gradient boosting, random forest and adaboost.
⟡ Apache SINGA (https://singa.apache.org) - An Apache Incubating project for developing an open source machine learning library.
⟡ Bayesian Methods for Hackers (https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python.
⟡ Featureforge (https://github.com/machinalis/featureforge) A set of tools for creating and testing machine learning features, with a scikit-learn compatible API.
@@ -1218,8 +1221,8 @@
⟡ metric-learn (https://github.com/metric-learn/metric-learn) - A Python module for metric learning.
⟡ OpenMetricLearning (https://github.com/OML-Team/open-metric-learning) - A PyTorch-based framework to train and validate the models producing high-quality embeddings.
⟡ Intel(R) Extension for Scikit-learn (https://github.com/intel/scikit-learn-intelex) - A seamless way to speed up your Scikit-learn applications with no accuracy loss and code changes.
⟡ SimpleAI (https://github.com/simpleai-team/simpleai) Python implementation of many of the artificial intelligence algorithms described in the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, 
well documented and tested library.
⟡ SimpleAI (https://github.com/simpleai-team/simpleai) Python implementation of many of the artificial intelligence algorithms described in the book "Artificial Intelligence, a Modern Approach". It focuses on providing an easy to use, well 
documented and tested library.
⟡ astroML (https://www.astroml.org/) - Machine Learning and Data Mining for Astronomy.
⟡ graphlab-create (https://turi.com/products/create/docs/) - A library with various machine learning models (regression, clustering, recommender systems, graph analytics, etc.) implemented on top of a disk-backed DataFrame.
⟡ BigML (https://bigml.com) - A library that contacts external servers.
@@ -1231,6 +1234,9 @@
⟡ hebel (https://github.com/hannes-brt/hebel) - GPU-Accelerated Deep Learning Library in Python. Deprecated 
⟡ Chainer (https://github.com/chainer/chainer) - Flexible neural network framework.
⟡ prophet (https://facebook.github.io/prophet/) - Fast and automated time series forecasting framework by Facebook.
⟡ skforecast (https://github.com/skforecast/skforecast) - Python library for time series forecasting using machine learning models. It works with any regressor compatible with the scikit-learn API, including popular options like LightGBM, 
XGBoost, CatBoost, Keras, and many others.
⟡ Feature-engine (https://github.com/feature-engine/feature_engine) - Open source library with an exhaustive battery of feature engineering and selection methods based on pandas and scikit-learn.
⟡ gensim (https://github.com/RaRe-Technologies/gensim) - Topic Modelling for Humans.
⟡ tweetopic (https://centre-for-humanities-computing.github.io/tweetopic/) - Blazing fast short-text-topic-modelling for Python.
⟡ topicwizard (https://github.com/x-tabdeveloping/topic-wizard) - Interactive topic model visualization/interpretation framework.
@@ -1243,15 +1249,15 @@
⟡ Crab (https://github.com/muricoca/crab) - A flexible, fast recommender engine. Deprecated 
⟡ python-recsys (https://github.com/ocelma/python-recsys) - A Python library for implementing a Recommender System.
⟡ thinking bayes (https://github.com/AllenDowney/ThinkBayes) - Book on Bayesian Analysis.
⟡ Image-to-Image Translation with Conditional Adversarial Networks (https://github.com/williamFalcon/pix2pix-keras) - Implementation of image to image (pix2pix) translation from the paper by isola et al 
(https://arxiv.org/pdf/1611.07004.pdf).DEEP LEARNING 
⟡ Image-to-Image Translation with Conditional Adversarial Networks (https://github.com/williamFalcon/pix2pix-keras) - Implementation of image to image (pix2pix) translation from the paper by isola et al (https://arxiv.org/pdf/1611.07004.pdf).DEEP
LEARNING 
⟡ Restricted Boltzmann Machines (https://github.com/echen/restricted-boltzmann-machines) -Restricted Boltzmann Machines in Python. DEEP LEARNING 
⟡ Bolt (https://github.com/pprett/bolt) - Bolt Online Learning Toolbox. Deprecated 
⟡ CoverTree (https://github.com/patvarilly/CoverTree) - Python implementation of cover trees, near-drop-in replacement for scipy.spatial.kdtree Deprecated 
⟡ nilearn (https://github.com/nilearn/nilearn) - Machine learning for NeuroImaging in Python.
⟡ neuropredict (https://github.com/raamana/neuropredict) - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of 
predictive performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to 
learn their API and code to produce anything useful.
⟡ neuropredict (https://github.com/raamana/neuropredict) - Aimed at novice machine learners and non-expert programmers, this package offers easy (no coding needed) and comprehensive machine learning (evaluation and full report of predictive 
performance WITHOUT requiring you to code) in Python for NeuroImaging and any other type of features. This is aimed at absorbing much of the ML workflow, unlike other packages like nilearn and pymvpa, which require you to learn their API and code
to produce anything useful.
⟡ imbalanced-learn (https://imbalanced-learn.org/stable/) - Python module to perform under sampling and oversampling with various techniques.
⟡ imbalanced-ensemble (https://github.com/ZhiningLiu1998/imbalanced-ensemble) - Python toolbox for quick implementation, modification, evaluation, and visualization of ensemble learning algorithms for class-imbalanced data. Supports 
out-of-the-box multi-class imbalanced (long-tailed) classification.
@@ -1260,12 +1266,12 @@
⟡ Caffe (https://github.com/BVLC/caffe) - A deep learning framework developed with cleanliness, readability, and speed in mind.
⟡ breze (https://github.com/breze-no-salt/breze) - Theano based library for deep and recurrent neural networks.
⟡ Cortex (https://github.com/cortexlabs/cortex) - Open source platform for deploying machine learning models in production.
⟡ pyhsmm (https://github.com/mattjj/pyhsmm) - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric 
extensions, the HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
⟡ pyhsmm (https://github.com/mattjj/pyhsmm) - library for approximate unsupervised inference in Bayesian Hidden Markov Models (HMMs) and explicit-duration Hidden semi-Markov Models (HSMMs), focusing on the Bayesian Nonparametric extensions, the 
HDP-HMM and HDP-HSMM, mostly with weak-limit approximations.
⟡ SKLL (https://github.com/EducationalTestingService/skll) - A wrapper around scikit-learn that makes it simpler to conduct experiments.
⟡ neurolab (https://github.com/zueve/neurolab)
⟡ Spearmint (https://github.com/HIPS/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. Deprecated 
⟡ Spearmint (https://github.com/HIPS/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. Deprecated 
⟡ Pebl (https://github.com/abhik/pebl/) - Python Environment for Bayesian Learning. Deprecated 
⟡ Theano (https://github.com/Theano/Theano/) - Optimizing GPU-meta-programming code generating array oriented optimizing math compiler in Python.
⟡ TensorFlow (https://github.com/tensorflow/tensorflow/) - Open source software library for numerical computation using data flow graphs.
@@ -1278,16 +1284,15 @@
⟡ Optunity (https://optunity.readthedocs.io/en/latest/) - A library dedicated to automated hyperparameter optimization with a simple, lightweight API to facilitate drop-in replacement of grid search.
⟡ Neural Networks and Deep Learning (https://github.com/mnielsen/neural-networks-and-deep-learning) - Code samples for my book "Neural Networks and Deep Learning" DEEP LEARNING .
⟡ Annoy (https://github.com/spotify/annoy) - Approximate nearest neighbours implementation.
⟡ TPOT (https://github.com/EpistasisLab/tpot) - Tool that automatically creates and optimizes machine learning pipelines using genetic programming. Consider it your personal data science assistant, automating a tedious part of machine 
learning.
⟡ TPOT (https://github.com/EpistasisLab/tpot) - Tool that automatically creates and optimizes machine learning pipelines using genetic programming. Consider it your personal data science assistant, automating a tedious part of machine learning.
⟡ pgmpy (https://github.com/pgmpy/pgmpy) A python library for working with Probabilistic Graphical Models.
⟡ DIGITS (https://github.com/NVIDIA/DIGITS) - The Deep Learning GPU Training System (DIGITS) is a web application for training deep learning models.
⟡ Orange (https://orange.biolab.si/) - Open source data visualization and data analysis for novices and experts.
⟡ MXNet (https://github.com/apache/incubator-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. Deprecated 
⟡ 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. Deprecated 
⟡ 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. 
Deprecated 
⟡ rgf_python (https://github.com/RGF-team/rgf) - Python bindings for Regularized Greedy Forest (Tree) Library.
⟡ skbayes (https://github.com/AmazaspShumik/sklearn-bayes) - Python package for Bayesian Machine Learning with scikit-learn API.
⟡ fuku-ml (https://github.com/fukuball/fuku-ml) - Simple machine learning library, including Perceptron, Regression, Support Vector Machine, Decision Tree and more, it's easy to use and easy to learn for beginners.
@@ -1299,22 +1304,22 @@
⟡ ML-From-Scratch (https://github.com/eriklindernoren/ML-From-Scratch) - Implementations of Machine Learning models from scratch in Python with a focus on transparency. Aims to showcase the nuts and bolts of ML in an accessible way.
⟡ Edward (http://edwardlib.org/) - A library for probabilistic modelling, inference, and criticism. Built on top of TensorFlow.
⟡ xRBM (https://github.com/omimo/xRBM) - A library for Restricted Boltzmann Machine (RBM) and its conditional variants in Tensorflow.
⟡ CatBoost (https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, well documented and supports CPU and GPU (even 
multi-GPU) computation.
⟡ CatBoost
 (https://github.com/catboost/catboost) - General purpose gradient boosting on decision trees library with categorical features support out of the box. It is easy to install, well documented and supports CPU and GPU (even multi-GPU) computation.
⟡ stacked_generalization (https://github.com/fukatani/stacked_generalization) - Implementation of machine learning stacking technique as a handy library in Python.
⟡ modAL (https://github.com/modAL-python/modAL) - A modular active learning framework for Python, built on top of scikit-learn.
⟡ Cogitare (https://github.com/cogitare-ai/cogitare): A Modern, Fast, and Modular Deep Learning and Machine Learning framework for Python.
⟡ Parris (https://github.com/jgreenemi/Parris) - Parris, the automated infrastructure setup tool for machine learning algorithms.
⟡ neonrvm (https://github.com/siavashserver/neonrvm) - neonrvm is an open source machine learning library based on RVM technique. It's written in C programming language and comes with Python programming language bindings.
⟡ Turi Create (https://github.com/apple/turicreate) - Machine learning from Apple. Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object 
detection, image classification, image similarity or activity classification to your app.
⟡ xLearn (https://github.com/aksnzhy/xlearn) - A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine 
learning problems on large-scale sparse data, which is very common in Internet services such as online advertisement and recommender systems.
⟡ Turi Create (https://github.com/apple/turicreate) - Machine learning from Apple. Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, 
image classification, image similarity or activity classification to your app.
⟡ xLearn (https://github.com/aksnzhy/xlearn) - A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning 
problems on large-scale sparse data, which is very common in Internet services such as online advertisement and recommender systems.
⟡ mlens (https://github.com/flennerhag/mlens) - A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.
⟡ Thampi (https://github.com/scoremedia/thampi) - Machine Learning Prediction System on AWS Lambda
⟡ MindsDB (https://github.com/mindsdb/mindsdb) - Open Source framework to streamline use of neural networks.
⟡ Microsoft Recommenders (https://github.com/Microsoft/Recommenders): Examples and best practices for building recommendation systems, provided as Jupyter notebooks. The repo contains some of the latest state of the art algorithms from 
Microsoft Research as well as from other companies and institutions.
⟡ Microsoft Recommenders (https://github.com/Microsoft/Recommenders): Examples and best practices for building recommendation systems, provided as Jupyter notebooks. The repo contains some of the latest state of the art algorithms from Microsoft 
Research as well as from other companies and institutions.
⟡ StellarGraph (https://github.com/stellargraph/stellargraph): Machine Learning on Graphs, a Python library for machine learning on graph-structured (network-structured) data.
⟡ BentoML (https://github.com/bentoml/bentoml): Toolkit for package and deploy machine learning models for serving in production
⟡ MiraiML (https://github.com/arthurpaulino/miraiml): An asynchronous engine for continuous & autonomous machine learning, built for real-time usage.
@@ -1322,15 +1327,14 @@
⟡ Neuraxle (https://github.com/Neuraxio/Neuraxle): A framework providing the right abstractions to ease research, development, and deployment of your ML pipelines.
⟡ Cornac (https://github.com/PreferredAI/cornac) - A comparative framework for multimodal recommender systems with a focus on models leveraging auxiliary data.
⟡ JAX (https://github.com/google/jax) - JAX is Autograd and XLA, brought together for high-performance machine learning research.
⟡ Catalyst (https://github.com/catalyst-team/catalyst) - High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. Being able to research/develop 
something new, rather than write another regular train loop.
⟡ Catalyst (https://github.com/catalyst-team/catalyst) - High-level utils for PyTorch DL & RL research. It was developed with a focus on reproducibility, fast experimentation and code/ideas reusing. Being able to research/develop something new, 
rather than write another regular train loop.
⟡ Fastai (https://github.com/fastai/fastai) - High-level wrapper built on the top of Pytorch which supports vision, text, tabular data and collaborative filtering.
⟡ scikit-multiflow (https://github.com/scikit-multiflow/scikit-multiflow) - A machine learning framework for multi-output/multi-label and stream data.
⟡ Lightwood
 (https://github.com/mindsdb/lightwood) - A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with objective to build predictive models with one line of code.
⟡ Lightwood (https://github.com/mindsdb/lightwood) - A Pytorch based framework that breaks down machine learning problems into smaller blocks that can be glued together seamlessly with objective to build predictive models with one line of code.
⟡ bayeso (https://github.com/jungtaekkim/bayeso) - A simple, but essential Bayesian optimization package, written in Python.
⟡ mljar-supervised (https://github.com/mljar/mljar-supervised) - An Automated Machine Learning (AutoML) python package for tabular data. It can handle: Binary Classification, MultiClass Classification and Regression. It provides 
explanations and markdown reports.
⟡ mljar-supervised
 (https://github.com/mljar/mljar-supervised) - An Automated Machine Learning (AutoML) python package for tabular data. It can handle: Binary Classification, MultiClass Classification and Regression. It provides explanations and markdown reports.
⟡ evostra (https://github.com/alirezamika/evostra) - A fast Evolution Strategy implementation in Python.
⟡ Determined (https://github.com/determined-ai/determined) - Scalable deep learning training platform, including integrated support for distributed training, hyperparameter tuning, experiment tracking, and model management.
⟡ PySyft (https://github.com/OpenMined/PySyft) - A Python library for secure and private Deep Learning built on PyTorch and TensorFlow.
@@ -1338,30 +1342,30 @@
⟡ sktime (https://github.com/alan-turing-institute/sktime) - A unified framework for machine learning with time series
⟡ OPFython (https://github.com/gugarosa/opfython) - A Python-inspired implementation of the Optimum-Path Forest classifier.
⟡ Opytimizer (https://github.com/gugarosa/opytimizer) - Python-based meta-heuristic optimization techniques.
⟡ Gradio (https://github.com/gradio-app/gradio) - A Python library for quickly creating and sharing demos of models. Debug models interactively in your browser, get feedback from collaborators, and generate public links without 
deploying anything.
⟡ Hub (https://github.com/activeloopai/Hub) - Fastest unstructured dataset management for TensorFlow/PyTorch. Stream & version-control data. Store even petabyte-scale data in a single numpy-like array on the cloud accessible on any 
machine. Visit activeloop.ai (https://activeloop.ai) for more info.
⟡ Gradio
 (https://github.com/gradio-app/gradio) - A Python library for quickly creating and sharing demos of models. Debug models interactively in your browser, get feedback from collaborators, and generate public links without deploying anything.
⟡ Hub (https://github.com/activeloopai/Hub) - Fastest unstructured dataset management for TensorFlow/PyTorch. Stream & version-control data. Store even petabyte-scale data in a single numpy-like array on the cloud accessible on any machine. Visit
activeloop.ai (https://activeloop.ai) for more info.
⟡ Synthia (https://github.com/dmey/synthia) - Multidimensional synthetic data generation in Python.
⟡ ByteHub (https://github.com/bytehub-ai/bytehub) - An easy-to-use, Python-based feature store. Optimized for time-series data.
⟡ Backprop (https://github.com/backprop-ai/backprop) - Backprop makes it simple to use, finetune, and deploy state-of-the-art ML models.
⟡ River (https://github.com/online-ml/river): A framework for general purpose online machine learning.
⟡ FEDOT (https://github.com/nccr-itmo/FEDOT): An AutoML framework for the automated design of composite modelling pipelines. It can handle classification, regression, and time series forecasting tasks on different types of data 
(including multi-modal datasets).
⟡ FEDOT (https://github.com/nccr-itmo/FEDOT): An AutoML framework for the automated design of composite modelling pipelines. It can handle classification, regression, and time series forecasting tasks on different types of data (including 
multi-modal datasets).
⟡ Sklearn-genetic-opt (https://github.com/rodrigo-arenas/Sklearn-genetic-opt): An AutoML package for hyperparameters tuning using evolutionary algorithms, with built-in callbacks, plotting, remote logging and more.
⟡ Evidently (https://github.com/evidentlyai/evidently): Interactive reports to analyze machine learning models during validation or production monitoring.
⟡ Streamlit (https://github.com/streamlit/streamlit): Streamlit is an framework to create beautiful data apps in hours, not weeks.
⟡ Optuna (https://github.com/optuna/optuna): Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning.
⟡ Deepchecks (https://github.com/deepchecks/deepchecks): Validation & testing of machine learning models and data during model development, deployment, and production. This includes checks and suites related to various types of issues, 
such as model performance, data integrity, distribution mismatches, and more.
⟡ Deepchecks (https://github.com/deepchecks/deepchecks): Validation & testing of machine learning models and data during model development, deployment, and production. This includes checks and suites related to various types of issues, such as 
model performance, data integrity, distribution mismatches, and more.
⟡ Shapash (https://github.com/MAIF/shapash) : Shapash is a Python library that provides several types of visualization that display explicit labels that everyone can understand.
⟡ Eurybia (https://github.com/MAIF/eurybia): Eurybia monitors data and model drift over time and securizes model deployment with data validation.
⟡ Colossal-AI (https://github.com/hpcaitech/ColossalAI): An open-source deep learning system for large-scale model training and inference with high efficiency and low cost.
⟡ dirty_cat (https://github.com/dirty-cat/dirty_cat) - facilitates machine-learning on dirty, non-curated categories. It provides transformers and encoders robust to morphological variants, such as typos.
⟡ Upgini (https://github.com/upgini/upgini): Free automated data & feature enrichment library for machine learning - automatically searches through thousands of ready-to-use features from public and community shared data sources and 
enriches your training dataset with only the accuracy improving features.
⟡ AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics (https://github.com/Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics): A tutorial to help machine learning researchers to automatically obtain 
optimized machine learning models with the optimal learning performance on any specific task.
⟡ skrub (https://github.com/skrub-data/skrub) - Skrub is a Python library that eases preprocessing and feature engineering for machine learning on dataframes.
⟡ Upgini (https://github.com/upgini/upgini): Free automated data & feature enrichment library for machine learning - automatically searches through thousands of ready-to-use features from public and community shared data sources and enriches your
training dataset with only the accuracy improving features.
⟡ AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics (https://github.com/Western-OC2-Lab/AutoML-Implementation-for-Static-and-Dynamic-Data-Analytics): A tutorial to help machine learning researchers to automatically obtain optimized 
machine learning models with the optimal learning performance on any specific task.
⟡ SKBEL (https://github.com/robinthibaut/skbel): A Python library for Bayesian Evidential Learning (BEL) in order to estimate the uncertainty of a prediction.
⟡ NannyML (https://bit.ly/nannyml-github-machinelearning): Python library capable of fully capturing the impact of data drift on performance. Allows estimation of post-deployment model performance without access to targets.
⟡ cleanlab (https://github.com/cleanlab/cleanlab): The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
@@ -1369,7 +1373,9 @@
⟡ PyBroker (https://github.com/edtechre/pybroker) - Algorithmic Trading with Machine Learning.
⟡ Frouros (https://github.com/IFCA/frouros): Frouros is an open source Python library for drift detection in machine learning systems.
⟡ CometML (https://github.com/comet-ml/comet-examples): The best-in-class MLOps platform with experiment tracking, model production monitoring, a model registry, and data lineage from training straight through to production.
⟡ Okrolearn (https://github.com/Okerew/okrolearn): A python machine learning library created to combine powefull data analasys features with tensors and machine learning components, while maintaining support for other libraries.
⟡ Opik (https://github.com/comet-ml/opik): Evaluate, trace, test, and ship LLM applications across your dev and production lifecycles.
⟡ pyclugen (https://github.com/clugen/pyclugen) - Multidimensional cluster generation in Python.
Data Analysis / Data Visualization
@@ -1386,8 +1392,7 @@
⟡ Pandas (https://pandas.pydata.org/) - A library providing high-performance, easy-to-use data structures and data analysis tools.
⟡ ParaMonte (https://github.com/cdslaborg/paramonte) - A general-purpose Python library for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found here 
(https://www.cdslab.org/paramonte/).
⟡ Vaex (https://github.com/vaexio/vaex) - A high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. Documentation can be found here 
(https://vaex.io/docs/index.html).
⟡ Vaex (https://github.com/vaexio/vaex) - A high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. Documentation can be found here (https://vaex.io/docs/index.html).
⟡ Open Mining (https://github.com/mining/mining) - Business Intelligence (BI) in Python (Pandas web interface) Deprecated 
⟡ PyMC (https://github.com/pymc-devs/pymc) - Markov Chain Monte Carlo sampling toolkit.
⟡ zipline (https://github.com/quantopian/zipline) - A Pythonic algorithmic trading library.
@@ -1429,20 +1434,21 @@
⟡ scikit-plot (https://github.com/reiinakano/scikit-plot) - A visualization library for quick and easy generation of common plots in data analysis and machine learning.
⟡ Bowtie (https://github.com/jwkvam/bowtie) - A dashboard library for interactive visualizations using flask socketio and react.
⟡ lime (https://github.com/marcotcr/lime) - Lime is about explaining what machine learning classifiers (or models) are doing. It is able to explain any black box classifier, with two or more classes.
⟡ PyCM (https://github.com/sepandhaghighi/pycm) - PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that 
supports most classes and overall statistics parameters
⟡ PyCM (https://github.com/sepandhaghighi/pycm) - PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports 
most classes and overall statistics parameters
⟡ Dash (https://github.com/plotly/dash) - A framework for creating analytical web applications built on top of Plotly.js, React, and Flask
⟡ Lambdo (https://github.com/asavinov/lambdo) - A workflow engine for solving machine learning problems by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table 
population and column evaluation via user-defined (Python) functions.
⟡ TensorWatch (https://github.com/microsoft/tensorwatch) - Debugging and visualization tool for machine learning and data science. It extensively leverages Jupyter Notebook to show real-time visualizations of data in running processes 
such as machine learning training.
⟡ Lambdo (https://github.com/asavinov/lambdo) - A workflow engine for solving machine learning problems by combining in one analysis pipeline (i) feature engineering and machine learning (ii) model training and prediction (iii) table population 
and column evaluation via user-defined (Python) functions.
⟡ TensorWatch (https://github.com/microsoft/tensorwatch) - Debugging and visualization tool for machine learning and data science. It extensively leverages Jupyter Notebook to show real-time visualizations of data in running processes such as 
machine learning training.
⟡ dowel (https://github.com/rlworkgroup/dowel) - A little logger for machine learning research. Output any object to the terminal, CSV, TensorBoard, text logs on disk, and more with just one call to logger.log().
⟡ Flama (https://github.com/vortico/flama) - Ignite your models into blazing-fast machine learning APIs with a modern framework.
Misc Scripts / iPython Notebooks / Codebases
⟡ MiniGrad (https://github.com/kennysong/minigrad) – A minimal, educational, Pythonic implementation of autograd (~100 loc).
⟡ Map/Reduce implementations of common ML algorithms (https://github.com/Yannael/BigDataAnalytics_INFOH515): Jupyter notebooks that cover how to implement from scratch different ML algorithms (ordinary least squares, gradient descent, 
k-means, alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.
⟡ Map/Reduce implementations of common ML algorithms (https://github.com/Yannael/BigDataAnalytics_INFOH515): Jupyter notebooks that cover how to implement from scratch different ML algorithms (ordinary least squares, gradient descent, k-means, 
alternating least squares), using Python NumPy, and how to then make these implementations scalable using Map/Reduce and Spark.
⟡ BioPy (https://github.com/jaredthecoder/BioPy) - Biologically-Inspired and Machine Learning Algorithms in Python. Deprecated 
⟡ CAEs for Data Assimilation (https://github.com/julianmack/Data_Assimilation) - Convolutional autoencoders for 3D image/field compression applied to reduced order Data Assimilation (https://en.wikipedia.org/wiki/Data_assimilation).
⟡ handsonml (https://github.com/ageron/handson-ml) - Fundamentals of machine learning in python.
@@ -1466,8 +1472,8 @@
⟡ sentiment-analyzer (https://github.com/madhusudancs/sentiment-analyzer) - Tweets Sentiment Analyzer
⟡ sentiment_classifier (https://github.com/kevincobain2000/sentiment_classifier) - Sentiment classifier using word sense disambiguation.
⟡ group-lasso (https://github.com/fabianp/group_lasso) - Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model.
⟡ jProcessing (https://github.com/kevincobain2000/jProcessing) - Kanji / Hiragana / Katakana to Romaji Converter. Edict Dictionary & parallel sentences Search. Sentence Similarity between two JP Sentences. Sentiment Analysis of Japanese
Text. Run Cabocha(ISO--8859-1 configured) in Python.
⟡ jProcessing (https://github.com/kevincobain2000/jProcessing) - Kanji / Hiragana / Katakana to Romaji Converter. Edict Dictionary & parallel sentences Search. Sentence Similarity between two JP Sentences. Sentiment Analysis of Japanese Text. Run
Cabocha(ISO--8859-1 configured) in Python.
⟡ mne-python-notebooks (https://github.com/mne-tools/mne-python-notebooks) - IPython notebooks for EEG/MEG data processing using mne-python.
⟡ Neon Course (https://github.com/NervanaSystems/neon_course) - IPython notebooks for a complete course around understanding Nervana's Neon.
⟡ pandas cookbook (https://github.com/jvns/pandas-cookbook) - Recipes for using Python's pandas library.
@@ -1479,8 +1485,8 @@
⟡ Python Programming for the Humanities (https://www.karsdorp.io/python-course/) - Course for Python programming for the Humanities, assuming no prior knowledge. Heavy focus on text processing / NLP.
⟡ GreatCircle (https://github.com/mwgg/GreatCircle) - Library for calculating great circle distance.
⟡ Optunity examples (http://optunity.readthedocs.io/en/latest/notebooks/index.html) - Examples demonstrating how to use Optunity in synergy with machine learning libraries.
⟡ 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."
⟡ 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.
@@ -1505,8 +1511,8 @@
⟡ nn_builder (https://github.com/p-christ/nn_builder) - nn_builder is a python package that lets you build neural networks in 1 line
⟡ NeuralTalk (https://github.com/karpathy/neuraltalk) - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences.
⟡ NeuralTalk (https://github.com/karpathy/neuraltalk2) - NeuralTalk is a Python+numpy project for learning Multimodal Recurrent Neural Networks that describe images with sentences. Deprecated 
⟡ Neuron (https://github.com/molcik/python-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. Deprecated 
⟡ Neuron (https://github.com/molcik/python-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. Deprecated 
⟡ Data Driven Code (https://github.com/atmb4u/data-driven-code) - Very simple implementation of neural networks for dummies in python without using any libraries, with detailed comments.
⟡ Machine Learning, Data Science and Deep Learning with Python
 (https://www.manning.com/livevideo/machine-learning-data-science-and-deep-learning-with-python) - LiveVideo course that covers machine learning, Tensorflow, artificial intelligence, and neural networks.
@@ -1539,16 +1545,16 @@
Kaggle Competition Source Code
⟡ open-solution-home-credit (https://github.com/neptune-ml/open-solution-home-credit) -> source code and experiments results (https://app.neptune.ml/neptune-ml/Home-Credit-Default-Risk) for Home Credit Default Risk 
(https://www.kaggle.com/c/home-credit-default-risk).
⟡ open-solution-googleai-object-detection (https://github.com/neptune-ml/open-solution-googleai-object-detection) -> source code and experiments results (https://app.neptune.ml/neptune-ml/Google-AI-Object-Detection-Challenge) for Google
AI Open Images - Object Detection Track (https://www.kaggle.com/c/google-ai-open-images-object-detection-track).
⟡ open-solution-googleai-object-detection (https://github.com/neptune-ml/open-solution-googleai-object-detection) -> source code and experiments results (https://app.neptune.ml/neptune-ml/Google-AI-Object-Detection-Challenge) for Google AI Open 
Images - Object Detection Track (https://www.kaggle.com/c/google-ai-open-images-object-detection-track).
⟡ open-solution-salt-identification (https://github.com/neptune-ml/open-solution-salt-identification) -> source code and experiments results (https://app.neptune.ml/neptune-ml/Salt-Detection) for TGS Salt Identification Challenge 
(https://www.kaggle.com/c/tgs-salt-identification-challenge).
⟡ open-solution-ship-detection (https://github.com/neptune-ml/open-solution-ship-detection) -> source code and experiments results (https://app.neptune.ml/neptune-ml/Ships) for Airbus Ship Detection Challenge 
(https://www.kaggle.com/c/airbus-ship-detection).
⟡ open-solution-data-science-bowl-2018 (https://github.com/neptune-ml/open-solution-data-science-bowl-2018) -> source code and experiments results (https://app.neptune.ml/neptune-ml/Data-Science-Bowl-2018) for 2018 Data Science Bowl 
(https://www.kaggle.com/c/data-science-bowl-2018).
⟡ open-solution-value-prediction (https://github.com/neptune-ml/open-solution-value-prediction) -> source code and experiments results (https://app.neptune.ml/neptune-ml/Santander-Value-Prediction-Challenge) for Santander Value 
Prediction Challenge (https://www.kaggle.com/c/santander-value-prediction-challenge).
⟡ open-solution-value-prediction (https://github.com/neptune-ml/open-solution-value-prediction) -> source code and experiments results (https://app.neptune.ml/neptune-ml/Santander-Value-Prediction-Challenge) for Santander Value Prediction 
Challenge (https://www.kaggle.com/c/santander-value-prediction-challenge).
⟡ open-solution-toxic-comments (https://github.com/neptune-ml/open-solution-toxic-comments) -> source code for Toxic Comment Classification Challenge (https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge).
⟡ 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".
@@ -1570,13 +1576,12 @@
Reinforcement Learning
⟡ DeepMind Lab (https://github.com/deepmind/lab) - DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. Its primary purpose is to act as a testbed for research in 
artificial intelligence, especially deep reinforcement learning.
⟡ DeepMind Lab (https://github.com/deepmind/lab) - DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. Its primary purpose is to act as a testbed for research in artificial
intelligence, especially deep reinforcement learning.
⟡ Gymnasium (https://github.com/Farama-Foundation/Gymnasium) - A library for developing and comparing reinforcement learning algorithms (successor of gym )(https://github.com/openai/gym).
⟡ Serpent.AI
 (https://github.com/SerpentAI/SerpentAI) - Serpent.AI is a game agent framework that allows you to turn any video game you own into a sandbox to develop AI and machine learning experiments. For both researchers and hobbyists.
⟡ ViZDoom (https://github.com/mwydmuch/ViZDoom) - ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep 
reinforcement learning, in particular.
⟡ Serpent.AI (https://github.com/SerpentAI/SerpentAI) - Serpent.AI is a game agent framework that allows you to turn any video game you own into a sandbox to develop AI and machine learning experiments. For both researchers and hobbyists.
⟡ ViZDoom (https://github.com/mwydmuch/ViZDoom) - ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement 
learning, in particular.
⟡ Roboschool (https://github.com/openai/roboschool) - Open-source software for robot simulation, integrated with OpenAI Gym.
⟡ Retro (https://github.com/openai/retro) - Retro Games in Gym
⟡ SLM Lab (https://github.com/kengz/SLM-Lab) - Modular Deep Reinforcement Learning framework in PyTorch.
@@ -1586,10 +1591,15 @@
⟡ acme (https://deepmind.com/research/publications/Acme) - An Open Source Distributed Framework for Reinforcement Learning that makes build and train your agents easily.
⟡ Spinning Up (https://spinningup.openai.com) - An educational resource designed to let anyone learn to become a skilled practitioner in deep reinforcement learning
⟡ Maze (https://github.com/enlite-ai/maze) - Application-oriented deep reinforcement learning framework addressing real-world decision problems.
⟡ RLlib
 (https://github.com/ray-project/ray) - RLlib is an industry level, highly scalable RL library for tf and torch, based on Ray. It's used by companies like Amazon and Microsoft to solve real-world decision making problems at scale.
⟡ DI-engine (https://github.com/opendilab/DI-engine) - DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms 
like QMIX in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.
⟡ RLlib (https://github.com/ray-project/ray) - RLlib is an industry level, highly scalable RL library for tf and torch, based on Ray. It's used by companies like Amazon and Microsoft to solve real-world decision making problems at scale.
⟡ DI-engine (https://github.com/opendilab/DI-engine) - DI-engine is a generalized Decision Intelligence engine. It supports most basic deep reinforcement learning (DRL) algorithms, such as DQN, PPO, SAC, and domain-specific algorithms like QMIX 
in multi-agent RL, GAIL in inverse RL, and RND in exploration problems.
⟡ Gym4ReaL (https://github.com/Daveonwave/gym4ReaL) - Gym4ReaL is a comprehensive suite of realistic environments designed to support the development and evaluation of RL algorithms that can operate in real-world scenarios. The suite includes a 
diverse set of tasks exposing RL algorithms to a variety of practical challenges.
Speech Recognition
⟡ EspNet (https://github.com/espnet/espnet) - ESPnet is an end-to-end speech processing toolkit for tasks like speech recognition, translation, and enhancement, using PyTorch and Kaldi-style data processing.
Ruby
@@ -1653,6 +1663,7 @@
⟡ RusticSOM (https://github.com/avinashshenoy97/RusticSOM) - A Rust library for Self Organising Maps (SOM).
⟡ candle (https://github.com/huggingface/candle) - Candle is a minimalist ML framework for Rust with a focus on performance (including GPU support) and ease of use.
⟡ linfa (https://github.com/rust-ml/linfa) - linfa aims to provide a comprehensive toolkit to build Machine Learning applications with Rust
⟡ delta (https://github.com/delta-rs/delta) - An open source machine learning framework in Rust Δ
Deep Learning
@@ -1688,8 +1699,8 @@
⟡ e1071 (https://cran.r-project.org/web/packages/e1071/index.html) - e1071: Misc Functions of the Department of Statistics (e1071), TU Wien
⟡ earth (https://cran.r-project.org/web/packages/earth/index.html) - earth: Multivariate Adaptive Regression Spline Models
⟡ elasticnet (https://cran.r-project.org/web/packages/elasticnet/index.html) - elasticnet: Elastic-Net for Sparse Estimation and Sparse PCA.
⟡ ElemStatLearn (https://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.
⟡ ElemStatLearn (https://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 (https://cran.r-project.org/web/packages/evtree/index.html) - evtree: Evolutionary Learning of Globally Optimal Trees.
⟡ forecast (https://cran.r-project.org/web/packages/forecast/index.html) - forecast: Timeseries forecasting using ARIMA, ETS, STLM, TBATS, and neural network models.
⟡ forecastHybrid (https://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.
@@ -1753,21 +1764,23 @@
⟡ tree (https://cran.r-project.org/web/packages/tree/index.html) - tree: Classification and regression trees.
⟡ varSelRF (https://cran.r-project.org/web/packages/varSelRF/index.html) - varSelRF: Variable selection using random forests.
⟡ XGBoost.R (https://github.com/tqchen/xgboost/tree/master/R-package) - R binding for eXtreme Gradient Boosting (Tree) Library.
⟡ Optunity (https://optunity.readthedocs.io/en/latest/) - 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.
⟡ Optunity (https://optunity.readthedocs.io/en/latest/) - 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 (https://igraph.org/r/) - binding to igraph library - General purpose graph library.
⟡ MXNet (https://github.com/apache/incubator-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 Modelling and Reporting (AMR).
⟡ clugenr (https://github.com/clugen/clugenr/) - Multidimensional cluster generation in R.
Data Manipulation | Data Analysis | Data Visualization
⟡ data.table (https://rdatatable.gitlab.io/data.table/) - data.table provides a high-performance version of base R’s data.frame with syntax and feature enhancements for ease of use, convenience and programming speed.
⟡ dplyr (https://www.rdocumentation.org/packages/dplyr/versions/0.7.8) - A data manipulation package that helps to solve the most common data manipulation problems.
⟡ ggplot2 (https://ggplot2.tidyverse.org/) - A data visualization package based on the grammar of graphics.
⟡ tmap (https://cran.r-project.org/web/packages/tmap/vignettes/tmap-getstarted.html) for visualizing geospatial data with static maps and leaflet (https://rstudio.github.io/leaflet/) for interactive maps
⟡ tm (https://www.rdocumentation.org/packages/tm/) and quanteda (https://quanteda.io/) are the main packages for managing, analyzing, and visualizing textual data.
⟡ shiny (https://shiny.rstudio.com/) is the basis for truly interactive displays and dashboards in R. However, some measure of interactivity can be achieved with htmlwidgets (https://www.htmlwidgets.org/) bringing javascript libraries 
to R. These include, plotly (https://plot.ly/r/), dygraphs (http://rstudio.github.io/dygraphs), highcharter (http://jkunst.com/highcharter/), and several others.
⟡ shiny (https://shiny.rstudio.com/) is the basis for truly interactive displays and dashboards in R. However, some measure of interactivity can be achieved with htmlwidgets (https://www.htmlwidgets.org/) bringing javascript libraries to R. These
include, plotly (https://plot.ly/r/), dygraphs (http://rstudio.github.io/dygraphs), highcharter (http://jkunst.com/highcharter/), and several others.
SAS
@@ -1775,8 +1788,8 @@
General-Purpose Machine Learning
⟡ Visual Data Mining and Machine Learning (https://www.sas.com/en_us/software/visual-data-mining-machine-learning.html) - Interactive, automated, and programmatic modelling with the latest machine learning algorithms in and end-to-end 
analytics environment, from data prep to deployment. Free trial available.
⟡ Visual Data Mining and Machine Learning (https://www.sas.com/en_us/software/visual-data-mining-machine-learning.html) - Interactive, automated, and programmatic modelling with the latest machine learning algorithms in and end-to-end analytics 
environment, from data prep to deployment. Free trial available.
⟡ Enterprise Miner (https://www.sas.com/en_us/software/enterprise-miner.html) - Data mining and machine learning that creates deployable models using a GUI or code.
⟡ Factory Miner (https://www.sas.com/en_us/software/factory-miner.html) - Automatically creates deployable machine learning models across numerous market or customer segments using a GUI.
@@ -1812,11 +1825,11 @@
⟡ ScalaNLP (http://www.scalanlp.org/) - ScalaNLP is a suite of machine learning and numerical computing libraries.
⟡ Breeze (https://github.com/scalanlp/breeze) - Breeze is a numerical processing library for Scala.
⟡ Chalk (https://github.com/scalanlp/chalk) - Chalk is a natural language processing library. Deprecated 
⟡ FACTORIE (https://github.com/factorie/factorie) - FACTORIE is a toolkit for deployable probabilistic modelling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor 
graphs, estimating parameters and performing inference.
⟡ FACTORIE (https://github.com/factorie/factorie) - FACTORIE is a toolkit for deployable probabilistic modelling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, 
estimating parameters and performing inference.
⟡ Montague (https://github.com/Workday/upshot-montague) - Montague is a semantic parsing library for Scala with an easy-to-use DSL.
⟡ Spark NLP (https://github.com/JohnSnowLabs/spark-nlp) - Natural language processing library built on top of Apache Spark ML to provide simple, performant, and accurate NLP annotations for machine learning pipelines, that scale easily 
in a distributed environment.
⟡ Spark NLP (https://github.com/JohnSnowLabs/spark-nlp) - Natural language processing library built on top of Apache Spark ML to provide simple, performant, and accurate NLP annotations for machine learning pipelines, that scale easily in a 
distributed environment.
Data Analysis / Data Visualization
@@ -1853,7 +1866,9 @@
⟡ SwiftLearner (https://github.com/valdanylchuk/swiftlearner/) - Simply written algorithms to help study ML or write your own implementations.
⟡ Smile (https://haifengl.github.io/) - Statistical Machine Intelligence and Learning Engine.
⟡ doddle-model (https://github.com/picnicml/doddle-model) - An in-memory machine learning library built on top of Breeze. It provides immutable objects and exposes its functionality through a scikit-learn-like API.
⟡ TensorFlow Scala (https://github.com/eaplatanios/tensorflow_scala) - Strongly-typed Scala API for TensorFlow.
⟡ TensorFlow Scala (https://github.com/eaplatanios/tensorflow_scala) - Strongly-typed Scala API for TensorFlow.
⟡ isolation-forest (https://github.com/linkedin/isolation-forest) - A distributed Spark/Scala implementation of the isolation forest algorithm for unsupervised outlier detection, featuring support for scalable training and ONNX export for easy 
cross-platform inference.
Scheme
@@ -1871,15 +1886,13 @@
⟡ Bender (https://github.com/xmartlabs/Bender) - Fast Neural Networks framework built on top of Metal. Supports TensorFlow models.
⟡ Swift AI (https://github.com/Swift-AI/Swift-AI) - Highly optimized artificial intelligence and machine learning library written in Swift.
⟡ Swift for Tensorflow
 (https://github.com/tensorflow/swift) - a next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond.
⟡ Swift for Tensorflow (https://github.com/tensorflow/swift) - a next-generation platform for machine learning, incorporating the latest research across machine learning, compilers, differentiable programming, systems design, and beyond.
⟡ BrainCore (https://github.com/alejandro-isaza/BrainCore) - The iOS and OS X neural network framework.
⟡ swix (https://github.com/stsievert/swix) - A bare bones library that includes a general matrix language and wraps some OpenCV for iOS development. Deprecated 
⟡ 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.
⟡ 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...
⟡ 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...
⟡ Perfect TensorFlow (https://github.com/PerfectlySoft/Perfect-TensorFlow) - Swift Language Bindings of TensorFlow. Using native TensorFlow models on both macOS / Linux.
⟡ PredictionBuilder (https://github.com/denissimon/prediction-builder-swift) - A library for machine learning that builds predictions using a linear regression.
⟡ Awesome CoreML (https://github.com/SwiftBrain/awesome-CoreML-models) - A curated list of pretrained CoreML models.
@@ -1904,45 +1917,48 @@
Misc
⟡ Wallaroo.AI (https://wallaroo.ai/) - Production AI plaftorm for deploying, managing, and observing any model at scale across any environment from cloud to edge. Let's go from python notebook to inferencing in minutes. 
⟡ Infinity (https://github.com/infiniflow/infinity) - The AI-native database built for LLM applications, providing incredibly fast vector and full-text search. Developed using C++20
⟡ Synthical (https://synthical.com) - AI-powered collaborative research environment. You can use it to get recommendations of articles based on reading history, simplify papers, find out what articles are trending, search articles by 
meaning (not just keywords), create and share folders of articles, see lists of articles from specific companies and universities, and add highlights.
⟡ Synthical (https://synthical.com) - AI-powered collaborative research environment. You can use it to get recommendations of articles based on reading history, simplify papers, find out what articles are trending, search articles by meaning (not
just keywords), create and share folders of articles, see lists of articles from specific companies and universities, and add highlights.
⟡ Humanloop (https://humanloop.com) – Humanloop is a platform for prompt experimentation, finetuning models for better performance, cost optimization, and collecting model generated data and user feedback.
⟡ Qdrant (https://qdrant.tech) – Qdrant is open source (https://github.com/qdrant/qdrant) vector similarity search engine with extended filtering support, written in Rust.
⟡ Localforge (https://localforge.dev/) – Is an open source (https://github.com/rockbite/localforge) on-prem AI coding autonomous assistant that lives inside your repo, edits and tests files at SSD speed. Think Claude Code but with UI. plug in any
LLM (OpenAI, Gemini, Ollama, etc.) and let it work for you.
⟡ milvus (https://milvus.io) – Milvus is open source (https://github.com/milvus-io/milvus) vector database for production AI, written in Go and C++, scalable and blazing fast for billions of embedding vectors.
⟡ Weaviate (https://www.semi.technology/developers/weaviate/current/) – Weaviate is an open source (https://github.com/semi-technologies/weaviate) vector search engine and vector database. Weaviate uses machine learning to vectorize and
store data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
⟡ Weaviate (https://www.semi.technology/developers/weaviate/current/) – Weaviate is an open source (https://github.com/semi-technologies/weaviate) vector search engine and vector database. Weaviate uses machine learning to vectorize and store 
data, and to find answers to natural language queries. With Weaviate you can also bring your custom ML models to production scale.
⟡ txtai (https://github.com/neuml/txtai) - Build semantic search applications and workflows.
⟡ MLReef (https://about.mlreef.com/) - MLReef is an end-to-end development platform using the power of git to give structure and deep collaboration possibilities to the ML development process.
⟡ Chroma (https://www.trychroma.com/) - Chroma - the AI-native open-source embedding database
⟡ Pinecone (https://www.pinecone.io/) - Vector database for applications that require real-time, scalable vector embedding and similarity search.
⟡ CatalyzeX (https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) - Browser extension (Chrome 
(https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) and Firefox (https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex/)) that automatically finds and shows code 
implementations for machine learning papers anywhere: Google, Twitter, Arxiv, Scholar, etc.
⟡ ML Workspace (https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a docker container and is preloaded with a variety of popular data science 
libraries (e.g., Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code).
⟡ Notebooks (https://github.com/rlan/notebooks) - A starter kit for Jupyter notebooks and machine learning. Companion docker images consist of all combinations of python versions, machine learning frameworks (Keras, PyTorch and 
Tensorflow) and CPU/CUDA versions.
⟡ CatalyzeX (https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) - Browser extension (Chrome (https://chrome.google.com/webstore/detail/code-finder-for-research/aikkeehnlfpamidigaffhfmgbkdeheil) 
and Firefox (https://addons.mozilla.org/en-US/firefox/addon/code-finder-catalyzex/)) that automatically finds and shows code implementations for machine learning papers anywhere: Google, Twitter, Arxiv, Scholar, etc.
⟡ ML Workspace (https://github.com/ml-tooling/ml-workspace) - All-in-one web-based IDE for machine learning and data science. The workspace is deployed as a docker container and is preloaded with a variety of popular data science libraries (e.g.,
Tensorflow, PyTorch) and dev tools (e.g., Jupyter, VS Code).
⟡ Notebooks (https://github.com/rlan/notebooks) - A starter kit for Jupyter notebooks and machine learning. Companion docker images consist of all combinations of python versions, machine learning frameworks (Keras, PyTorch and Tensorflow) and 
CPU/CUDA versions.
⟡ DVC (https://github.com/iterative/dvc) - Data Science Version Control is an open-source version control system for machine learning projects with pipelines support. It makes ML projects reproducible and shareable.
⟡ DVClive (https://github.com/iterative/dvclive) - Python library for experiment metrics logging into simply formatted local files.
⟡ VDP (https://github.com/instill-ai/vdp) - open source visual data ETL to streamline the end-to-end visual data processing pipeline: extract unstructured visual data from pre-built data sources, transform it into analysable structured 
insights by Vision AI models imported from various ML platforms, and load the insights into warehouses or applications.
⟡ VDP (https://github.com/instill-ai/vdp) - open source visual data ETL to streamline the end-to-end visual data processing pipeline: extract unstructured visual data from pre-built data sources, transform it into analysable structured insights 
by Vision AI models imported from various ML platforms, and load the insights into warehouses or applications.
⟡ Kedro (https://github.com/quantumblacklabs/kedro/) - Kedro is a data and development workflow framework that implements best practices for data pipelines with an eye towards productionizing machine learning models.
⟡ Hamilton (https://github.com/dagworks-inc/hamilton) - a lightweight library to define data transformations as a directed-acyclic graph (DAG). It helps author reliable feature engineering and machine learning pipelines, and more.
⟡ guild.ai (https://guild.ai/) - Tool to log, analyze, compare and "optimize" experiments. It's cross-platform and framework independent, and provided integrated visualizers such as tensorboard.
⟡ Sacred (https://github.com/IDSIA/sacred) - Python tool to help you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the 
proposed standard.
⟡ Comet (https://www.comet.com/) - ML platform for tracking experiments, hyper-parameters, artifacts and more. It's deeply integrated with over 15+ deep learning frameworks and orchestration tools. Users can also use the platform to 
monitor their models in production.
⟡ Sacred
 (https://github.com/IDSIA/sacred) - Python tool to help you configure, organize, log and reproduce experiments. Like a notebook lab in the context of Chemistry/Biology. The community has built multiple add-ons leveraging the proposed standard.
⟡ Comet (https://www.comet.com/) - ML platform for tracking experiments, hyper-parameters, artifacts and more. It's deeply integrated with over 15+ deep learning frameworks and orchestration tools. Users can also use the platform to monitor 
their models in production.
⟡ MLFlow (https://mlflow.org/) - platform to manage the ML lifecycle, including experimentation, reproducibility and deployment. Framework and language agnostic, take a look at all the built-in integrations.
⟡ Weights & Biases (https://www.wandb.com/) - Machine learning experiment tracking, dataset versioning, hyperparameter search, visualization, and collaboration
⟡ More tools to improve the ML lifecycle: Catalyst (https://github.com/catalyst-team/catalyst), PachydermIO (https://www.pachyderm.io/). The following are GitHub-alike and targeting teams Weights & Biases (https://www.wandb.com/), 
Neptune.ai (https://neptune.ai/), Comet.ml (https://www.comet.ml/), Valohai.ai (https://valohai.com/), DAGsHub (https://DAGsHub.com/).
⟡ Arize AI (https://www.arize.com) - Model validaiton and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
⟡ MachineLearningWithTensorFlow2ed (https://www.manning.com/books/machine-learning-with-tensorflow-second-edition) - a book on general purpose machine learning techniques regression, classification, unsupervised clustering, 
reinforcement learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.
⟡ More tools to improve the ML lifecycle: Catalyst (https://github.com/catalyst-team/catalyst), PachydermIO (https://www.pachyderm.io/). The following are GitHub-alike and targeting teams Weights & Biases (https://www.wandb.com/), Neptune.ai 
(https://neptune.ai/), Comet.ml (https://www.comet.ml/), Valohai.ai (https://valohai.com/), DAGsHub (https://DAGsHub.com/).
⟡ Arize AI (https://www.arize.com) - Model validation and performance monitoring, drift detection, explainability, visualization across structured and unstructured data
⟡ MachineLearningWithTensorFlow2ed (https://www.manning.com/books/machine-learning-with-tensorflow-second-edition) - a book on general purpose machine learning techniques regression, classification, unsupervised clustering, reinforcement 
learning, auto encoders, convolutional neural networks, RNNs, LSTMs, using TensorFlow 1.14.1.
⟡ m2cgen (https://github.com/BayesWitnesses/m2cgen) - A tool that allows the conversion of ML models into native code (Java, C, Python, Go, JavaScript, Visual Basic, C#, R, PowerShell, PHP, Dart) with zero dependencies.
⟡ CML (https://github.com/iterative/cml) - A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual 
reports with metrics and graphs in pull/merge requests. Framework & language agnostic.
⟡ CML (https://github.com/iterative/cml) - A library for doing continuous integration with ML projects. Use GitHub Actions & GitLab CI to train and evaluate models in production like environments and automatically generate visual reports with 
metrics and graphs in pull/merge requests. Framework & language agnostic.
⟡ Pythonizr (https://pythonizr.com) - An online tool to generate boilerplate machine learning code that uses scikit-learn.
⟡ Flyte (https://flyte.org/) - Flyte makes it easy to create concurrent, scalable, and maintainable workflows for machine learning and data processing.
⟡ Chaos Genius (https://github.com/chaos-genius/chaos_genius/) - ML powered analytics engine for outlier/anomaly detection and root cause analysis.
@@ -1950,26 +1966,35 @@
⟡ DockerDL (https://github.com/matifali/dockerdl) - Ready to use deeplearning docker images.
⟡ Aqueduct (https://github.com/aqueducthq/aqueduct) - Aqueduct enables you to easily define, run, and manage AI & ML tasks on any cloud infrastructure.
⟡ Ambrosia (https://github.com/reactorsh/ambrosia) - Ambrosia helps you clean up your LLM datasets using _other_ LLMs.
⟡ Fiddler AI (https://www.fiddler.ai) - The all-in-one AI Observability and Security platform for responsible AI. It provides monitoring, analytics, and centralized controls to operationalize ML, GenAI, and LLM applications with trust. Fiddler 
helps enterprises scale LLM and ML deployments to deliver high performance AI, reduce costs, and be responsible in governance.
⟡ Maxim AI (https://getmaxim.ai) - The agent simulation, evaluation, and observability platform helping product teams ship their AI applications with the quality and speed needed for real-world use.
⟡ Agentic Radar (https://github.com/splx-ai/agentic-radar) - Open-source CLI security scanner for agentic workflows. Scans your workflow’s source code, detects vulnerabilities, and generates an interactive visualization along with a detailed 
security report. Supports LangGraph, CrewAI, n8n, OpenAI Agents, and more.
Books
⟡ Distributed Machine Learning Patterns (https://github.com/terrytangyuan/distributed-ml-patterns) - This book teaches you how to take machine learning models from your personal laptop to large distributed clusters. You’ll explore key 
concepts and patterns behind successful distributed machine learning systems, and learn technologies like TensorFlow, Kubernetes, Kubeflow, and Argo Workflows directly from a key maintainer and contributor, with real-world scenarios and
hands-on projects.
⟡ Distributed Machine Learning Patterns (https://github.com/terrytangyuan/distributed-ml-patterns) - This book teaches you how to take machine learning models from your personal laptop to large distributed clusters. You’ll explore key concepts 
and patterns behind successful distributed machine learning systems, and learn technologies like TensorFlow, Kubernetes, Kubeflow, and Argo Workflows directly from a key maintainer and contributor, with real-world scenarios and hands-on projects.
⟡ Grokking Machine Learning (https://www.manning.com/books/grokking-machine-learning) - Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and high school-level math.
⟡ Machine Learning Bookcamp (https://www.manning.com/books/machine-learning-bookcamp) - Learn the essentials of machine learning by completing a carefully designed set of real-world projects.
⟡ Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1098125975) - Through a recent series of breakthroughs, deep learning has boosted the entire 
field of machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal 
theory, and production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
⟡ Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1098125975) - Through a recent series of breakthroughs, deep learning has boosted the entire field of 
machine learning. Now, even programmers who know close to nothing about this technology can use simple, efficient tools to implement programs capable of learning from data. This bestselling book uses concrete examples, minimal theory, and 
production-ready Python frameworks (Scikit-Learn, Keras, and TensorFlow) to help you gain an intuitive understanding of the concepts and tools for building intelligent systems.
⟡ Machine Learning Books for Beginners
 (https://www.appliedaicourse.com/blog/machine-learning-books/) - This blog provides a curated list of introductory books to help aspiring ML professionals to grasp foundational machine learning concepts and techniques.
⟡ Netron (https://netron.app/) - An opensource viewer for neural network, deep learning and machine learning models
⟡ Teachable Machine (https://teachablemachine.withgoogle.com/) - Train Machine Learning models on the fly to recognize your own images, sounds, & poses.
⟡ Pollinations.AI (https://pollinations.ai) - Free, no-signup APIs for text, image, and audio generation with no API keys required. Offers OpenAI-compatible interfaces and React hooks for easy integration.
⟡ Model Zoo (https://modelzoo.co/) - Discover open source deep learning code and pretrained models.
Credits
⟡ Some of the python libraries were cut-and-pasted from vinta (https://github.com/vinta/awesome-python)
⟡ References for Go were mostly cut-and-pasted from gopherdata (https://github.com/gopherdata/resources/tree/master/tooling)
machinelearning Github: https://github.com/josephmisiti/awesome-machine-learning