Dive into Deep Learning

Livro digital

Título:
Dive into Deep Learning

Autor:
Aston Zhang, Zachary C. Lipton, Mu Li, Alexander J. Smola

Categoria:
Tecnologia > IA

Doador:
Raffaello D. N.

Sinopse:
Most machine learning books open with theory and leave the code as an afterthought; this one starts its very first chapter with "A Motivating Example" and only then walks backward into "Key Components," "Kinds of Machine Learning Problems," and a chapter called "The Road to Deep Learning" that treats the field's history as something to be traced rather than assumed. Before a single neural network appears, the second chapter spends its opening pages on tensor indexing, broadcasting, and memory handling, treating the mechanics of the math as seriously as the math itself. Across more than a thousand pages, the book pairs every mathematical notation with runnable code, moving from linear algebra, calculus, and probability through data preprocessing, into the full arc of modern deep learning architectures. Each numbered subsection ends in a short summary and a set of exercises, so the structure doubles as a semester-length curriculum rather than a reference to be skimmed once. The chapters are written to run against multiple deep learning frameworks, which is why sections on "Conversion to Other Python Objects" and "Conversion to the Tensor Format" show up as early, unglamorous building blocks rather than footnotes. Written by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola and released under a Creative Commons Attribution-ShareAlike license, the book has become one of the most widely adopted open deep learning textbooks precisely because it refuses to separate the proof from the implementation. It rewards readers who want to type along as much as those who want to understand why the math works, and it holds up equally well as a first course or as a reference to return to once the basics are no longer new.