The ChessAIThon project (2025-1-ES01-KA220-VET-000354329) is co-funded by the European Union. The views and opinions expressed in this publication are those of the author(s) only and do not necessarily reflect those of the European Union or the Spanish Service for the Internationalisation of Education (SEPIE). Neither the European Union nor the National Agency SEPIE can be held responsible for them.
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Efficiency with Parquet
Finally, explain that the transformed, numerical data is saved in a highly compressed Parquet format. This format is superior to CSV for massive datasets, ensuring the data is quickly and efficiently loaded for the intensive work of training an AI model. This entire process illustrates how raw data is meticulously prepared for advanced machine learning algorithms.
Important: Crucially, our transformation strategy is directly inspired by the architecture of AlphaZero. Each chess position is converted into a 77x8x8 Board Representation, a dense, multi-layered tensor that numerically encodes all necessary game information—piece locations, legal moves, and state details—as a standardized grid of zeros and ones. This data structure is paramount for deep learning models.
To grasp the specifics of this AlphaZero-inspired solution, teachers and students must consult the shared Kaggle Notebook: