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[https://en.wikipedia.org/wiki/Artificial_neural_network Artificial Neural Networks] ('''ANNs''') are a family of [https://en.wikipedia.org/wiki/Machine_learning statistical learning] devices or algorithms used in [https://en.wikipedia.org/wiki/Regression_analysis regression], and [https://en.wikipedia.org/wiki/Binary_classification binary] or [[https://en.wikipedia.org/wiki/Multiclass_classification multiclass classification|multiclass classification]], implemented in [[Hardware|hardware]] or [[Software|software]] inspired by their biological counterparts. The [https://en.wikipedia.org/wiki/Artificial_neuron artificial neurons] of one or more layers receive one or more inputs (representing dendrites), and after being weighted, sum them to produce an output (representing a neuron's axon). The sum is passed through a [https://en.wikipedia.org/wiki/Nonlinear_system nonlinear] function known as an [https://en.wikipedia.org/wiki/Activation_function activation function] or transfer function. The transfer functions usually have a [https://en.wikipedia.org/wiki/Sigmoid_function sigmoid shape], but they may also take the form of other non-linear functions, [https://en.wikipedia.org/wiki/Piecewise piecewise] linear functions, or [https://en.wikipedia.org/wiki/Artificial_neuron#Step_function step functions] <ref>[https://en.wikipedia.org/wiki/Artificial_neuron Artificial neuron from Wikipedia]</ref>. The weights of the inputs of each layer are tuned to minimize a [https://en.wikipedia.org/wiki/Loss_function cost or loss function], which is a task in [https://en.wikipedia.org/wiki/Mathematical_optimization mathematical optimization] and machine learning.

==Residual NetsNet==

A '''Residual netsnet''' add (ResNet) adds the input of a layer, typically composed of a convolutional layer and of a [https://en.wikipedia.org/wiki/Rectifier_(neural_networks) ReLU] layer, to its output. This modification, like convolutional nets inspired from image classification, enables faster training and deeper networks <ref>[[Tristan Cazenave]] ('''2017'''). ''[http://ieeexplore.ieee.org/document/7875402/ Residual Networks for Computer Go]''. [[IEEE#TOCIAIGAMES|IEEE Transactions on Computational Intelligence and AI in Games]], Vol. PP, No. 99, [http://www.lamsade.dauphine.fr/~cazenave/papers/resnet.pdf pdf]</ref> <ref>[https://wiki.tum.de/display/lfdv/Deep+Residual+Networks Deep Residual Networks] from [https://wiki.tum.de/ TUM Wiki], [[Technical University of Munich]]</ref> <ref>[https://towardsdatascience.com/understanding-and-visualizing-resnets-442284831be8 Understanding and visualizing ResNets] by Pablo Ruiz, October 8, 2018</ref>.

and [[Kristallweizen-kai]] <ref>[https://github.com/Tama4649/Kristallweizen/ GitHub - Tama4649/Kristallweizen: 第29回世界コンピュータ将棋選手権 準優勝のKristallweizenです。]</ref>, apparently with [[AlphaZero]] strength <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=72754 The Stockfish of shogi] by [[Larry Kaufman]], [[CCC]], January 07, 2020</ref>. [[Hisayori Noda|Nodchip]] incorporated NNUE into the chess playing Stockfish 10 as a proof of concept <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=74059 Stockfish NN release (NNUE)] by [[Henk Drost]], [[CCC]], May 31, 2020</ref>, yielding in the hype about [[Stockfish NNUE]] in summer 2020 <ref>[http://yaneuraou.yaneu.com/2020/06/19/stockfish-nnue-the-complete-guide/ Stockfish NNUE – The Complete Guide], June 19, 2020 (Japanese and English)</ref>.