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an adaptation of [[Gian-Carlo Pascutto|Gian-Carlo Pascutto's]] [[Leela Zero]] [[Go]] project <ref>[https://github.com/gcp/leela-zero GitHub - gcp/leela-zero: Go engine with no human-provided knowledge, modeled after the AlphaGo Zero paper]</ref> to [[Chess]], using [[Stockfish|Stockfish's]] [[Board Representation|board representation]] and [[Move Generation|move generation]]. No heuristics or prior [[Knowledge|knowledge]] are carried over from Stockfish. The goal to build a strong [[UCT]] chess AI following the same type of [[Deep Learning|deep learning]] techniques of [[AlphaZero]] as described in [[DeepMind|DeepMind's]] paper <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>, but using distributed training for the weights of the [[Neural Networks#Deep|deep]] [[Neural Networks#Residual|residual]] [[Neural Networks#Convolutional|convolutional neural network]]. The training process requires [https://en.wikipedia.org/wiki/CUDA CUDA] and a [[GPU]] accelerated version of [https://en.wikipedia.org/wiki/TensorFlow Tensorflow] installed <ref>[https://github.com/glinscott/leela-chess/blob/master/README.md leela-chess/README.md at master · glinscott/leela-chess · GitHub]</ref>.