Deep Learning with Python (3rd ed.) — Author Notes

Chollet's preface and author notes for the third edition of Deep Learning with Python, updated to cover the transformer era. The notes reflect on how much has changed since the first edition in 2017 — attention mechanisms have displaced RNNs entirely, transformers are the universal architecture, and the question has shifted from "can we build useful AI?" to "how do we understand and align it?" Chollet discusses why he included new material on the limitations of deep learning, the importance of interpretability, and the distinction between statistical learning and reasoning that motivates his ARC work. The book remains the most-read practical deep learning textbook.