algorithms provides educational videos, lessons, tutorials, or self-learning resources.
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Algorithms Books provides educational videos, lessons, tutorials, or self-learning resources.
Algorithms-Cheatsheet-Resources is a GitHub repository or organization with source code, releases, documentation, or project resources.
CP-Algorithms is a developer resource for coding, documentation, communities, security research, or software tools.
JavaScript Algorithms is a GitHub repository or organization with source code, releases, documentation, or project resources.
Sorting Algorithms Visualizer provides educational videos, lessons, tutorials, or self-learning resources.
The Algorithms is a GitHub repository or organization with source code, releases, documentation, or project resources.
The Algorithms - C++ helps developers write, search, review, test, or manage code and projects.
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.
Stanford research project that streams free over-the-air U.S. television channels while studying video-delivery algorithms.
Helps find, analyze or synthesize information for research and knowledge discovery.
OpenCV tool that combines image processing algorithms to remove black-bar caption filters from Snapchat images
Online learning algorithms (Perceptron, AROW, SCW, Logistic Regression).
Reinforcement Learning Coach by IntelĀ® 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.
Helps teams build, deploy, observe or operate machine-learning systems.
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.