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Typical CNN <ref>Typical [https://en.wikipedia.org/wiki/Convolutional_neural_network CNN] architecture, Image by Aphex34, December 16, 2015, [https://creativecommons.org/licenses/by-sa/4.0/deed.en CC BY-SA 4.0], [https://en.wikipedia.org/wiki/Wikimedia_Commons Wikimedia Commons]</ref>
<span id="Residual"></span>
==Residual NetsNet==
[[FILE:ResiDualBlock.png|border|right|thumb|link=https://arxiv.org/abs/1512.03385| A residual block <ref>The fundamental building block of residual networks. Figure 2 in [https://scholar.google.com/citations?user=DhtAFkwAAAAJ Kaiming He], [https://scholar.google.com/citations?user=yuB-cfoAAAAJ&hl=en Xiangyu Zhang], [http://shaoqingren.com/ Shaoqing Ren], [http://www.jiansun.org/ Jian Sun] ('''2015'''). ''Deep Residual Learning for Image Recognition''. [https://arxiv.org/abs/1512.03385 arXiv:1512.03385]</ref> <ref>[https://blog.waya.ai/deep-residual-learning-9610bb62c355 Understand Deep Residual Networks — a simple, modular learning framework that has redefined state-of-the-art] by [https://blog.waya.ai/@waya.ai Michael Dietz], [https://blog.waya.ai/ Waya.ai], May 02, 2017</ref> ]]
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>.
=ANNs in Games=
: [https://towardsdatascience.com/types-of-convolutions-in-deep-learning-717013397f4d An Introduction to different Types of Convolutions in Deep Learning] by [http://plpp.de/ Paul-Louis Pröve], July 22, 2017
: [https://towardsdatascience.com/squeeze-and-excitation-networks-9ef5e71eacd7 Squeeze-and-Excitation Networks] by [http://plpp.de/ Paul-Louis Pröve], October 17, 2017
* [https://towardsdatascience.com/deep-convolutional-neural-networks-ccf96f830178 Deep Convolutional Neural Networks] by Pablo Ruiz, October 11, 2018
===ResNet===
* [https://en.wikipedia.org/wiki/Residual_neural_network Residual neural network from Wikipedia]
* [https://wiki.tum.de/display/lfdv/Deep+Residual+Networks Deep Residual Networks] from [https://wiki.tum.de/ TUM Wiki], [[Technical University of Munich]]
* [https://towardsdatascience.com/understanding-and-visualizing-resnets-442284831be8 Understanding and visualizing ResNets] by Pablo Ruiz, October 8, 2018
===RNNs===
* [https://en.wikipedia.org/wiki/Recurrent_neural_network Recurrent neural network from Wikipedia]
