Deep learning and computer chess - Ding, CongCong (Nanyang Technological University, 2022)
This 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.