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dc.contributor.authorDing, CongCongen_US
dc.identifier.citationDing, C. (2022). Deep learning and computer chess. Final Year Project (FYP), Nanyang Technological University, Singapore.
dc.description.abstractThis report presents two supervised learning approach for training neural networks to evaluate chess positions. The architecture used to build the neural network model is based on the Giraffe’s architecture [2] and Stockfish NNUE -HalfKP [3]. Implemented a method to train a neural network architecture to understand chess movement and techniques that a grandmaster would play. Both approaches implemented as a 7-class classification problem on a dataset of over 10,000 samples games. We collected different chess game played by grandmaster, then used the evaluation function of stockfish [5], one of the strongest existing chess engines, to get the score of the positions and label it accordingly. We extracted the positions from the games using Forsyth-Edwards notation and stored them in csv files which are later used for training the model.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleDeep learning and computer chessen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorHe Yingen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Engineering)en_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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