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.
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Deconstructing the AI Myth: Fallacies and Harms of Algorithmification provides machine-learning models, research, training resources, or evaluation…
Code for hyperparameter tuning/optimization of machine learning and deep learning algorithms.
Free AI writing and agent tools: Text Auditor, Humanizer, CLAUDE.md Writer, SKILL.md Generator, AI Writing Coach, and more. No account required.
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.
Can you spot the DeepFake? Detect Fakes challenges you to discern AI-manipulated videos from real videos. Can you do better than an algorithm?
Efficient pure-Matlab implementations of graph algorithms to complement MatlabBGL's mex functions.
Graph layout algorithms in pure Julia.
Some experiments with the coordinate descent algorithm used in the (Sparse) Group Lasso model.
A library for developing and comparing reinforcement learning algorithms (successor of [gym])(https://github.com/openai/gym).
Implementation of the hdbscan algorithm in Python - used for clustering.
Python examples of popular machine learning algorithms with interactive Jupyter demos and math being explained.
General Machine Learning library using Numenta’s Cortical Learning Algorithm.
Helps teams build, deploy, observe or operate machine-learning systems.
JavaScript implementation of the k nearest neighbors algorithm for supervised learning.
Just a simple implementation of K-Nearest Neighbors algorithm using with a bunch of similarity measures.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Introduction to machine learning covering basic theory, algorithms and applications.
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.