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=ANNs=
[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.
==Perceptron==
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=
<span id="AlphaZero"></span>
===Alpha Zero===
In December 2017, the [[Google]] [[DeepMind]] team along with former [[Giraffe]] author [[Matthew Lai]] reported on their generalized [[AlphaZero]] algorithm, combining [[Deep Learning|Deep learning]] with [[Monte-Carlo Tree Search]]. AlphaZero can achieve, tabula rasa, superhuman performance in many challenging domains with some training effort. Starting from random play, and given no domain knowledge except the game rules, AlphaZero achieved a superhuman level of play in the games of chess and [[Shogi]] as well as Go, and convincingly defeated a world-champion program in each case <ref>[[David Silver]], [[Thomas Hubert]], [[Julian Schrittwieser]], [[Ioannis Antonoglou]], [[Matthew Lai]], [[Arthur Guez]], [[Marc Lanctot]], [[Laurent Sifre]], [[Dharshan Kumaran]], [[Thore Graepel]], [[Timothy Lillicrap]], [[Karen Simonyan]], [[Demis Hassabis]] ('''2017'''). ''Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm''. [https://arxiv.org/abs/1712.01815 arXiv:1712.01815]</ref>. The open souece projects [[Leela Zero]] (Go) and its chess adaptation [[Leela Chess Zero]] successfully re-implemented the ideas of DeepMind.===NNUE===[[NNUE]] reverse of ƎUИИ - Efficiently Updatable Neural Networks, is an NN architecture intended to replace the [[Evaluation|evaluation]] of [[Shogi]], [[Chess|chess]] and other board game playing [[Alpha-Beta|alpha-beta]] searchers. NNUE was introduced in 2018 by [[Yu Nasu]] <ref>[[Yu Nasu]] ('''2018'''). ''ƎUИИ Efficiently Updatable Neural-Network based Evaluation Functions for Computer Shogi''. Ziosoft Computer Shogi Club, [https://github.com/ynasu87/nnue/blob/master/docs/nnue.pdf pdf] (Japanese with English abstract) [https://github.com/asdfjkl/nnue GitHub - asdfjkl/nnue translation]</ref>,and was used in Shogi adaptations of [[Stockfish]] such as [[YaneuraOu]] <ref>[https://github.com/yaneurao/YaneuraOu GitHub - yaneurao/YaneuraOu: YaneuraOu is the World's Strongest Shogi engine(AI player), WCSC29 1st winner, educational and USI compliant engine]</ref> ,and [[Kristallweizen]] <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>.Its heavily over parametrized computational most expensive input layer is efficiently [[Incremental Updates|incremental updated]] in [[Make Move|make]] and [[Unmake Move|unmake move]].
<span id="engines"></span>
===NN Chess Programs===
* [[Memory]]
* [[Neural MoveMap Heuristic]]
* [[NNUE]]
* [[Pattern Recognition]]
* [[David E. Moriarty#SANE|SANE]]
* [[Temporal Difference Learning]]
'''2004'''
* [http://dblp.uni-trier.de/pers/hd/p/Patist:Jan_Peter Jan Peter Patist], [[Marco Wiering]] ('''2004'''). ''Learning to Play Draughts using Temporal Difference Learning with Neural Networks and Databases''. [http://students.uu.nl/en/hum/cognitive-artificial-intelligence Cognitive Artificial Intelligence], [https://en.wikipedia.org/wiki/Utrecht_University Utrecht University], Benelearn’04
* [[Henk Mannen]], [[Marco Wiering]] ('''2004'''). ''[https://www.semanticscholar.org/paper/Learning-to-Play-Chess-using-TD(lambda)-learning-Mannen-Wiering/00a6f81c8ebe8408c147841f26ed27eb13fb07f3 Learning to play chess using TD(λ)-learning with database games]''. [http://students.uu.nl/en/hum/cognitive-artificial-intelligence Cognitive Artificial Intelligence], [https://en.wikipedia.org/wiki/Utrecht_University Utrecht University], Benelearn’04, [https://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/learning-chess.pdf pdf]
* [[Mathieu Autonès]], [[Aryel Beck]], [[Phillippe Camacho]], [[Nicolas Lassabe]], [[Hervé Luga]], [[François Scharffe]] ('''2004'''). ''[http://link.springer.com/chapter/10.1007/978-3-540-24650-3_1 Evaluation of Chess Position by Modular Neural network Generated by Genetic Algorithm]''. [http://www.informatik.uni-trier.de/~ley/db/conf/eurogp/eurogp2004.html#AutonesBCLLS04 EuroGP 2004] <ref>[https://www.stmintz.com/ccc/index.php?id=358770 Presentation for a neural net learning chess program] by [[Dann Corbit]], [[CCC]], April 06, 2004</ref>
* [[Daniel Walker]], [[Robert Levinson]] ('''2004'''). ''The MORPH Project in 2004''. [[ICGA Journal#27_4|ICGA Journal, Vol. 27, No. 4]]
* [[Christopher Clark]], [[Amos Storkey]] ('''2014'''). ''Teaching Deep Convolutional Neural Networks to Play Go''. [http://arxiv.org/abs/1412.3409 arXiv:1412.3409] <ref>[http://computer-go.org/pipermail/computer-go/2014-December/007010.html Teaching Deep Convolutional Neural Networks to Play Go] by [[Hiroshi Yamashita]], [http://computer-go.org/pipermail/computer-go/ The Computer-go Archives], December 14, 2014</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=54663 Teaching Deep Convolutional Neural Networks to Play Go] by [[Michel Van den Bergh]], [[CCC]], December 16, 2014</ref>
* [[Chris J. Maddison]], [[Shih-Chieh Huang|Aja Huang]], [[Ilya Sutskever]], [[David Silver]] ('''2014'''). ''Move Evaluation in Go Using Deep Convolutional Neural Networks''. [http://arxiv.org/abs/1412.6564v1 arXiv:1412.6564v1] » [[Go]]
* [[Ilya Sutskever]], [https://research.google.com/pubs/OriolVinyals.html [Oriol Vinyals]], [https://www.linkedin.com/in/quoc-v-le-319b5a8 [Quoc V. Le]] ('''2014'''). ''Sequence to Sequence Learning with Neural Networks''. [https://arxiv.org/abs/1409.3215 arXiv:1409.3215]
'''2015'''
* [https://scholar.google.nl/citations?user=yyIoQu4AAAAJ Diederik P. Kingma], [https://scholar.google.ca/citations?user=ymzxRhAAAAAJ&hl=en Jimmy Lei Ba] ('''2015'''). ''Adam: A Method for Stochastic Optimization''. [https://arxiv.org/abs/1412.6980v8 arXiv:1412.6980v8], [http://www.iclr.cc/doku.php?id=iclr2015:main ICLR 2015] <ref>[http://www.talkchess.com/forum/viewtopic.php?t=61948 Arasan 19.2] by [[Jon Dart]], [[CCC]], November 03, 2016 » [[Arasan#Tuning|Arasan's Tuning]]</ref>
* [http://michaelnielsen.org/ Michael Nielsen] ('''2015'''). ''[http://neuralnetworksanddeeplearning.com/ Neural networks and deep learning]''. Determination Press
* [[Mathematician#SIoffe|Sergey Ioffe]], [[Mathematician#CSzegedy|Christian Szegedy]] ('''2015'''). ''Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift''. [https://arxiv.org/abs/1502.03167 arXiv:1502.03167]
* [[Mathematician#GEHinton|Geoffrey E. Hinton]], [https://research.google.com/pubs/OriolVinyals.html [Oriol Vinyals]], [https://en.wikipedia.org/wiki/Jeff_Dean_(computer_scientist) Jeff Dean] ('''2015'''). ''Distilling the Knowledge in a Neural Network''. [https://arxiv.org/abs/1503.02531 arXiv:1503.02531]
* [[James L. McClelland]] ('''2015'''). ''[https://web.stanford.edu/group/pdplab/pdphandbook/handbook3.html#handbookch10.html Explorations in Parallel Distributed Processing: A Handbook of Models, Programs, and Exercises]''. Second Edition, [https://web.stanford.edu/group/pdplab/pdphandbook/handbookli1.html Contents]
* [[Gábor Melis]] ('''2015'''). ''[http://jmlr.org/proceedings/papers/v42/meli14.html Dissecting the Winning Solution of the HiggsML Challenge]''. [https://nips.cc/Conferences/2014 NIPS 2014]
* [[Matthew Lai]] ('''2015'''). ''Giraffe: Using Deep Reinforcement Learning to Play Chess''. M.Sc. thesis, [https://en.wikipedia.org/wiki/Imperial_College_London Imperial College London], [http://arxiv.org/abs/1509.01549v1 arXiv:1509.01549v1] » [[Giraffe]]
* [[Nikolai Yakovenko]], [[Liangliang Cao]], [[Colin Raffel]], [[James Fan]] ('''2015'''). ''Poker-CNN: A Pattern Learning Strategy for Making Draws and Bets in Poker Games''. [https://arxiv.org/abs/1509.06731 arXiv:1509.06731]
* [https://scholar.google.ca/citations?user=yVtSOt8AAAAJ&hl=en Emmanuel Bengio], [https://scholar.google.ca/citations?user=9H77FYYAAAAJ&hl=en Pierre-Luc Bacon], [[Joelle Pineau]], [[Doina Precup]] ('''2015'''). ''Conditional Computation in Neural Networks for faster models''. [https://arxiv.org/abs/1511.06297 arXiv:1511.06297]
* [[Ilya Loshchilov]], [[Frank Hutter]] ('''2015'''). ''Online Batch Selection for Faster Training of Neural Networks''. [https://arxiv.org/abs/1511.06343 arXiv:1511.06343]
* [[Yuandong Tian]], [[Yan Zhu]] ('''2015'''). ''Better Computer Go Player with Neural Network and Long-term Prediction''. [http://arxiv.org/abs/1511.06410 arXiv:1511.06410] <ref>[http://www.technologyreview.com/view/544181/how-facebooks-ai-researchers-built-a-game-changing-go-engine/?utm_campaign=socialsync&utm_medium=social-post&utm_source=facebook How Facebook’s AI Researchers Built a Game-Changing Go Engine | MIT Technology Review], December 04, 2015</ref> <ref>[http://www.talkchess.com/forum/viewtopic.php?t=58514 Combining Neural Networks and Search techniques (GO)] by Michael Babigian, [[CCC]], December 08, 2015</ref> » [[Go]]
* [[Audrūnas Gruslys]], [[Rémi Munos]], [[Ivo Danihelka]], [[Marc Lanctot]], [[Alex Graves]] ('''2016'''). ''Memory-Efficient Backpropagation Through Time''. [https://arxiv.org/abs/1606.03401v1 arXiv:1606.03401]
* [[Mathematician#AARusu|Andrei A. Rusu]], [[Neil C. Rabinowitz]], [[Guillaume Desjardins]], [[Hubert Soyer]], [[James Kirkpatrick]], [[Koray Kavukcuoglu]], [[Mathematician#RPascanu|Razvan Pascanu]], [[Mathematician#RHadsell|Raia Hadsell]] ('''2016'''). ''Progressive Neural Networks''. [https://arxiv.org/abs/1606.04671 arXiv:1606.04671]
* [[Gao Huang]], [[Zhuang Liu]], [[Laurens van der Maaten]], [[Kilian Q. Weinberger]] ('''2016'''). ''Densely Connected Convolutional Networks''. [https://arxiv.org/abs/1608.06993 arXiv:1608.06993] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=75665&start=9 Re: Minic version 3] by [[Connor McMonigle]], [[CCC]], November 03, 2020 » [[Minic#Minic 3|Minic 3]], [[Seer|Seer 1.1]]</ref>
* [[George Rajna]] ('''2016'''). ''Deep Neural Networks''. [http://vixra.org/abs/1609.0126 viXra:1609.0126]
* [[James Kirkpatrick]], [[Mathematician#RPascanu|Razvan Pascanu]], [[Neil C. Rabinowitz]], [[Joel Veness]], [[Guillaume Desjardins]], [[Mathematician#AARusu|Andrei A. Rusu]], [[Kieran Milan]], [[John Quan]], [[Tiago Ramalho]], [[Agnieszka Grabska-Barwinska]], [[Demis Hassabis]], [[Claudia Clopath]], [[Dharshan Kumaran]], [[Mathematician#RHadsell|Raia Hadsell]] ('''2016'''). ''Overcoming catastrophic forgetting in neural networks''. [https://arxiv.org/abs/1612.00796 arXiv:1612.00796] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=70704 catastrophic forgetting] by [[Daniel Shawul]], [[CCC]], May 09, 2019</ref>
* [https://dblp.uni-trier.de/pers/hd/n/Niu:Zhenxing Zhenxing Niu], [https://dblp.uni-trier.de/pers/hd/z/Zhou:Mo Mo Zhou], [https://dblp.uni-trier.de/pers/hd/w/Wang_0003:Le Le Wang], [[Xinbo Gao]], [https://dblp.uni-trier.de/pers/hd/h/Hua_0001:Gang Gang Hua] ('''2016'''). ''Ordinal Regression with Multiple Output CNN for Age Estimation''. [https://dblp.uni-trier.de/db/conf/cvpr/cvpr2016.html CVPR 2016], [https://www.cv-foundation.org/openaccess/content_cvpr_2016/app/S21-20.pdf pdf]
* [[Li Jing]], [[Yichen Shen]], [[Tena Dubček]], [[John Peurifoy]], [[Scott Skirlo]], [[Mathematician#YLeCun|Yann LeCun]], [[Max Tegmark]], [[Marin Soljačić]] ('''2016'''). ''Tunable Efficient Unitary Neural Networks (EUNN) and their application to RNNs''. [https://arxiv.org/abs/1612.05231 arXiv:1612.05231] <ref>[http://talkchess.com/forum3/viewtopic.php?f=2&t=74059 Stockfish NN release (NNUE)] by [[Henk Drost]], [[CCC]], May 31, 2020 » [[Stockfish]]</ref>
'''2017'''
* [[Yutian Chen]], [[Matthew W. Hoffman]], [[Sergio Gomez Colmenarejo]], [[Misha Denil]], [[Timothy Lillicrap]], [[Matthew Botvinick]], [[Nando de Freitas]] ('''2017'''). ''Learning to Learn without Gradient Descent by Gradient Descent''. [https://arxiv.org/abs/1611.03824v6 arXiv:1611.03824v6], [http://dblp.uni-trier.de/db/conf/icml/icml2017.html ICML 2017]
* [https://dblp.org/pers/hd/s/Serb:Alexander Alexantrou Serb], [[Edoardo Manino]], [https://dblp.org/pers/hd/m/Messaris:Ioannis Ioannis Messaris], [https://dblp.org/pers/hd/t/Tran=Thanh:Long Long Tran-Thanh], [https://www.orc.soton.ac.uk/people/tp1f12 Themis Prodromakis] ('''2017'''). ''[https://eprints.soton.ac.uk/425616/ Hardware-level Bayesian inference]''. [https://nips.cc/Conferences/2017 NIPS 2017] » [[Analog Evaluation]]
'''2018'''
* [[Yu Nasu]] ('''2018'''). ''ƎUИИ Efficiently Updatable Neural-Network based Evaluation Functions for Computer Shogi''. Ziosoft Computer Shogi Club, [https://github.com/ynasu87/nnue/blob/master/docs/nnue.pdf pdf], [https://www.apply.computer-shogi.org/wcsc28/appeal/the_end_of_genesis_T.N.K.evolution_turbo_type_D/nnue.pdf pdf] (Japanese with English abstract) [https://github.com/asdfjkl/nnue GitHub - asdfjkl/nnue translation] » [[NNUE]] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=2&t=76250 Translation of Yu Nasu's NNUE paper] by [[Dominik Klein]], [[CCC]], January 07, 2021</ref>
* [[Kei Takada]], [[Hiroyuki Iizuka]], [[Masahito Yamamoto]] ('''2018'''). ''[https://link.springer.com/chapter/10.1007%2F978-3-319-75931-9_2 Computer Hex Algorithm Using a Move Evaluation Method Based on a Convolutional Neural Network]''. [https://link.springer.com/bookseries/7899 Communications in Computer and Information Science] » [[Hex]]
* [[Matthia Sabatelli]], [[Francesco Bidoia]], [[Valeriu Codreanu]], [[Marco Wiering]] ('''2018'''). ''Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead''. ICPRAM 2018, [http://www.ai.rug.nl/~mwiering/GROUP/ARTICLES/ICPRAM_CHESS_DNN_2018.pdf pdf]
* [[Marius Lindauer]], [[Frank Hutter]] ('''2019'''). ''Best Practices for Scientific Research on Neural Architecture Search''. [https://arxiv.org/abs/1909.02453 arXiv:1909.02453]
* [[Guy Haworth]] ('''2019'''). ''Chess endgame news: an endgame challenge for neural nets''. [[ICGA Journal#41_3|ICGA Journal, Vol. 41, No. 3]] » [[Endgame]]
==2020 ...==
* [[Reid McIlroy-Young]], [[Siddhartha Sen]], [[Jon Kleinberg]], [[Ashton Anderson]] ('''2020'''). ''Aligning Superhuman AI with Human Behavior: Chess as a Model System''. [[ACM#SIGKDD|ACM SIGKDD 2020]], [https://arxiv.org/abs/2006.01855 arXiv:2006.01855] » [[Maia Chess]]
* [[Reid McIlroy-Young]], [[Russell Wang]], [[Siddhartha Sen]], [[Jon Kleinberg]], [[Ashton Anderson]] ('''2020'''). ''Learning Personalized Models of Human Behavior in Chess''. [https://arxiv.org/abs/2008.10086 arXiv:2008.10086]
* [[Oisín Carroll]], [[Joeran Beel]] ('''2020'''). ''Finite Group Equivariant Neural Networks for Games''. [https://arxiv.org/abs/2009.05027 arXiv:2009.05027]
* [https://scholar.google.com/citations?user=HT85tXsAAAAJ&hl=en Mohammad Pezeshki], [https://scholar.google.com/citations?user=jKqh8jAAAAAJ&hl=en Sékou-Oumar Kaba], [[Mathematician#YBengio|Yoshua Bengio]] , [[Mathematician#ACourville|Aaron Courville]] , [[Doina Precup]], [https://scholar.google.com/citations?user=ifu_7_0AAAAJ&hl=en Guillaume Lajoie] ('''2020'''). ''Gradient Starvation: A Learning Proclivity in Neural Networks''. [https://arxiv.org/abs/2011.09468 arXiv:2011.09468]
=Blog & Forum Posts=
==2020 ...==
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74077 How to work with batch size in neural network] by Gertjan Brouwer, [[CCC]], June 02, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74531 NNUE accessible explanation] by [[Martin Fierz]], [[CCC]], July 21, 2020 » [[NNUE]]
: [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74531&start=1 Re: NNUE accessible explanation] by [[Jonathan Rosenthal]], [[CCC]], July 23, 2020
: [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74531&start=5 Re: NNUE accessible explanation] by [[Jonathan Rosenthal]], [[CCC]], July 24, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=74607 LC0 vs. NNUE - some tech details...] by [[Srdja Matovic]], [[CCC]], July 29, 2020 » [[Leela Chess Zero#Lc0|Lc0]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74771 AB search with NN on GPU...] by [[Srdja Matovic]], [[CCC]], August 13, 2020 » [[GPU]] <ref>[https://forums.developer.nvidia.com/t/kernel-launch-latency/62455 kernel launch latency - CUDA / CUDA Programming and Performance - NVIDIA Developer Forums] by LukeCuda, June 18, 2018</ref>
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74777 Neural Networks weights type] by [[Fabio Gobbato]], [[CCC]], August 13, 2020 » [[Stockfish NNUE]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=74955 Train a neural network evaluation] by [[Fabio Gobbato]], [[CCC]], September 01, 2020 » [[Automated Tuning]], [[NNUE]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75042 Neural network quantization] by [[Fabio Gobbato]], [[CCC]], September 08, 2020 » [[NNUE]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75190 First success with neural nets] by [[Jonathan Kreuzer]], [[CCC]], September 23, 2020
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=75606 Transhuman Chess with NN and RL...] by [[Srdja Matovic]], [[CCC]], October 30, 2020 » [[Reinforcement Learning|RL]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75724 Pytorch NNUE training] by [[Gary Linscott]], [[CCC]], November 08, 2020 <ref>[https://en.wikipedia.org/wiki/PyTorch PyTorch from Wikipedia]</ref> » [[NNUE]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75925 Pawn King Neural Network] by [[Tamás Kuzmics]], [[CCC]], November 26, 2020 » [[NNUE]]
* [http://laatste.info/bb3/viewtopic.php?f=53&t=8327 Learning draughts evaluation functions using Keras/TensorFlow] by [[Rein Halbersma]], [http://laatste.info/bb3/viewforum.php?f=53 World Draughts Forum], November 30, 2020 » [[Draughts]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=75985 Maiachess] by [[Marc-Philippe Huget]], [[CCC]], December 04, 2020 » [[Maia Chess]]
'''2021'''
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76263 More experiments with neural nets] by [[Jonathan Kreuzer]], [[CCC]], January 09, 2021 » [[Slow Chess]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76334 Keras/Tensforflow for very sparse inputs] by Jacek Dermont, [[CCC]], January 16, 2021
* [http://www.talkchess.com/forum3/viewtopic.php?f=2&t=76664 Are neural nets (the weights file) copyrightable?] by [[Adam Treat]], [[CCC]], February 21, 2021
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76885 A worked example of backpropagation using Javascript] by [[Colin Jenkins]], [[CCC]], March 16, 2021 » [[Neural Networks#Backpropagation|Backpropagation]]
* [http://www.talkchess.com/forum3/viewtopic.php?f=7&t=77061 yet another NN library] by lucasart, [[CCC]], April 11, 2021 » [[#lucasart|lucasart/nn]]
=External Links=
: [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]
* [https://en.wikipedia.org/wiki/Rectifier_(neural_networks) Rectifier (neural networks) from Wikipedia]
* [https://en.wikipedia.org/wiki/Sigmoid_function Sigmoid function from Wikipedia]
* [https://en.wikipedia.org/wiki/Softmax_function Softmax function from Wikipedia]
==Backpropagation==
* [https://en.wikipedia.org/wiki/Backpropagation Backpropagation from Wikipedia]
* [https://en.wikipedia.org/wiki/Rprop Rprop from Wikipedia]
* [http://people.idsia.ch/~juergen/who-invented-backpropagation.html Who Invented Backpropagation?] by [[Jürgen Schmidhuber]] (2014, 2015)
* [https://alexander-schiendorfer.github.io/2020/02/24/a-worked-example-of-backprop.html A worked example of backpropagation] by [https://alexander-schiendorfer.github.io/about.html Alexander Schiendorfer], February 24, 2020 » [[Neural Networks#Backpropagation|Backpropagation]] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=76885 A worked example of backpropagation using Javascript] by [[Colin Jenkins]], [[CCC]], March 16, 2021</ref>
==Gradient==
* [https://en.wikipedia.org/wiki/Gradient Gradient from Wikipedia]
: [https://en.wikipedia.org/wiki/SNNS SNNS from Wikipedia]
* [https://en.wikipedia.org/wiki/Comparison_of_deep_learning_software Comparison of deep learning software from Wikipedia]
* [https://github.com/connormcmonigle/reference-neural-network GitHub - connormcmonigle/reference-neural-network] by [[Connor McMonigle]]
* <span id="lucasart"></span>[https://github.com/lucasart/nn GitHub - lucasart/nn: neural network experiment] <ref>[http://www.talkchess.com/forum3/viewtopic.php?f=7&t=77061 yet another NN library] by lucasart, [[CCC]], April 11, 2021</ref>
==Libraries==
* [https://en.wikipedia.org/wiki/Eigen_%28C%2B%2B_library%29 Eigen (C++ library) from Wikipedia]
* [http://leenissen.dk/fann/wp/ Fast Artificial Neural Network Library (FANN)]
* [https://en.wikipedia.org/wiki/Keras Keras from Wikipedia]
* [https://wiki.python.org/moin/PythonForArtificialIntelligence PythonForArtificialIntelligence - Python Wiki] [[Python]]
* [https://en.wikipedia.org/wiki/TensorFlow TensorFlow from Wikipedia]
: [https://www.youtube.com/watch?v=9KM9Td6RVgQ Part 6: Training]
: [https://www.youtube.com/watch?v=S4ZUwgesjS8 Part 7: Overfitting, Testing, and Regularization]
* [https://www.youtube.com/playlist?list=PLgomWLYGNl1dL1Qsmgumhcg4HOcWZMd3k NN - Fully Connected Tutorial], [https://en.wikipedia.org/wiki/YouTube YouTube] Videos by [[Finn Eggers]]
* [https://www.youtube.com/watch?v=UdSK7nnJKHU Deep Learning Master Class] by [[Ilya Sutskever]], [https://en.wikipedia.org/wiki/YouTube YouTube] Video
* [https://www.youtube.com/watch?v=Ih5Mr93E-2c&hd=1 Lecture 10 - Neural Networks] from [http://work.caltech.edu/telecourse.html Learning From Data - Online Course (MOOC)] by [https://en.wikipedia.org/wiki/Yaser_Abu-Mostafa Yaser Abu-Mostafa], [https://en.wikipedia.org/wiki/California_Institute_of_Technology Caltech], [https://en.wikipedia.org/wiki/YouTube YouTube] Video
: [https://www.youtube.com/watch?v=lvoHnicueoE Lecture 14 | Deep Reinforcement Learning] by [[Mathematician#SYeung|Serena Yeung]], [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture14.pdf slides]
: [https://www.youtube.com/watch?v=eZdOkDtYMoo Lecture 15 | Efficient Methods and Hardware for Deep Learning] by [https://scholar.google.com/citations?user=E0iCaa4AAAAJ&hl=en Song Han], [http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture15.pdf slides]
=References=
<references />
'''[[Learning|Up one Level]]'''
[[Category:Videos]]
