Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/162926
Title: Deep learning and computer chess (part 2)
Authors: Seah, Yu Liang
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Seah, Y. L. (2022). Deep learning and computer chess (part 2). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/162926
Project: SCSE21-0733 
Abstract: A machine would need more than 10^90 years to make the first chess move using brute force method. To address this problem, various strategies based on minimax algorithms and deep learning advancements have surfaced throughout time. One innovation that is able to address this problem is giraffe architecture. The data set was carefully chosen from chess matches between different grandmasters from around the world. To test the idea and see how the models react, various Giraffe models with different parameter settings are created.
URI: https://hdl.handle.net/10356/162926
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)

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