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The most publicly known application of machine learning in games is likely the use of deep learning agents that compete with professional human players in complex strategy games. There has been a significant application of machine learning on games such as Atari/ALE, Doom, Minecraft, StarCraft, and car racing. [1]
scikit-learn (formerly scikits.learn and also known as sklearn) is a free and open-source machine learning library for the Python programming language. [3] It features various classification, regression and clustering algorithms including support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific ...
Pygame was originally written by Pete Shinners to replace PySDL after its development stalled. [2] [8] It has been a community project since 2000 [9] and is released under the free software GNU Lesser General Public License [5] (which "provides for Pygame to be distributed with open source and commercial software" [10]).
SageMath - a system for algebra and geometry experimentation via Python. Scilab - free open-source software for numerical computation and simulation similar to MATLAB/Simulink. Sim4Life.lite - online version of Sim4Life that is free-of-charge for students for team-learning and online collaboration with classmates and teachers on limited size ...
Godot (/ ˈ ɡ ɒ d oʊ / GOD-oh) [a] is a cross-platform, free and open-source game engine released under the permissive MIT license.It was initially developed in Buenos Aires by Argentine software developers Juan Linietsky and Ariel Manzur [6] for several companies in Latin America prior to its public release in 2014. [7]
The SVM learning code from both libraries is often reused in other open source machine learning toolkits, including GATE, KNIME, Orange [3] and scikit-learn. [4] Bindings and ports exist for programming languages such as Java, MATLAB, R, Julia, and Python. It is available in e1071 library in R and scikit-learn in Python.
The Computer Language Benchmarks Game (formerly called The Great Computer Language Shootout) is a free software project for comparing how a given subset of simple algorithms can be implemented in various popular programming languages. The project consists of: A set of very simple algorithmic problems
The first deep learning multilayer perceptron trained by stochastic gradient descent [28] was published in 1967 by Shun'ichi Amari. [29] In computer experiments conducted by Amari's student Saito, a five layer MLP with two modifiable layers learned internal representations to classify non-linearily separable pattern classes. [10]