‘Principles and Practice of Deep Representation Learning’ proposes a theory of how deep networks learn

Co-author Yi Ma frames the book and an accompanying course around three ideas: learning aims to find data representations that maximize information gain; deep network architectures are unrolled iterative optimization algorithms; and updates to network parameters aim to make those algorithms more efficient. The authors say the open-source textbook uses optimization and information theory to make deep networks easier to understand.