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Advances in Evolutionary Algorithms. Edited by: Witold Kosinski. ISBN 978-953-7619-11-4, PDF ISBN 978-953-51-5796-0, Published 2008-11-01.

Code for hyperparameter tuning/optimization of machine learning and deep learning algorithms.

Helps teams build, deploy, observe or operate machine-learning systems.

Introduction to machine learning covering basic theory, algorithms and applications.

Helps find, analyze or synthesize information for research and knowledge discovery.

Online learning algorithms (Perceptron, AROW, SCW, Logistic Regression).

Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms.

Data Science at Home is a podcast about machine learning, artificial intelligence, and algorithms.

Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL's mex functions.

Graph layout algorithms in pure Julia.

A library for developing and comparing reinforcement learning algorithms (successor of [gym])(https://github.com/openai/gym).

Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.

Algorithms for learning and inference with discrete probabilistic models.

A Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback.

About helping professional programmers confidently apply machine learning algorithms to address complex problems.

Open-source project that supports software development with code generation, analysis, debugging, or documentation.

Metric Learning Algorithms in Python.

A set of functions to support the development of machine learning algorithms.

Framework that implements AutoML algorithms for model architecture search at scale.

Parris, the automated infrastructure setup tool for machine learning algorithms.

This package contains the matlab implementation of the algorithms described in the book Pattern Recognition and Machine Learning by C. Bishop.

Machine Learning library for PHP. Algorithms, Cross Validation, Neural Network, Preprocessing, Feature Extraction and much more in one library.

Python Machine Learning Pentesting Toolbox for Adversarial Attacks. Works with LLMs, DNNs, and other machine learning algorithms.

Basic sampling algorithms for Julia.