Viyugam-64.RO1

I recently built a simple yet exciting RNN-based chess move predictor that learns patterns from thousands of recorded chess games. The idea was to use a Recurrent Neural Network to understand sequential move patterns and predict the next best move based on previous positions and actions. By converting chess games into a sequence-friendly numeric format and training an RNN model on them, the network gradually learned common strategies, opening lines, and mid-game tactics. While it’s not a full chess engine, the project was a great hands-on introduction to using RNNs for sequence modeling, and it showed how deep learning can capture complex decision-making patterns even in a game as rich as chess.