Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162874
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dc.contributor.authorDing, CongCongen_US
dc.date.accessioned2022-11-11T06:53:24Z-
dc.date.available2022-11-11T06:53:24Z-
dc.date.issued2022-
dc.identifier.citationDing, C. (2022). Deep learning and computer chess. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162874en_US
dc.identifier.urihttps://hdl.handle.net/10356/162874-
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.language.isoenen_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
dc.contributor.supervisoremailYHe@ntu.edu.sgen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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