This paper argues that a scientific theory of deep learning is emerging, which the authors call "learning mechanics." They identify five key research areas contributing to this theory: solvable idealized settings, tractable limits, simple mathematical laws, hyperparameter theories, and universal behaviors. The proposed theory focuses on training dynamics, aggregate statistics, and quantitative predictions rather than detailed mechanisms.