The neural network of the Stockfish chess engine

This blog post delves into the neural network architecture used by the Stockfish chess engine, one of the strongest chess programs in the world. It explains how Stockfish evaluates chess positions and selects moves using a combination of traditional algorithms and deep learning techniques. The post provides an overview of the neural network structure, which consists of multiple layers and uses a convolutional neural network (CNN) approach. It also discusses the training process, including the use of self-play and reinforcement learning. The author highlights the impressive performance of Stockfish and how its neural network has significantly improved its playing strength.