Understanding Deep Learning

An authoritative, accessible, and uptodate treatment of deep learning that strikes a pragmatic middle ground between theory and practice.Deep learning is a fastmoving field with sweeping relevance in todays increasingly digital world. Understanding Deep Learning provides an authoritative, accessible, and uptodate treatment of the subject, covering all the key topics along with recent advances and cuttingedge concepts. Many deep learning texts are crowded with technical details that obscure fundamentals, but Simon Prince ruthlessly curates only the most important ideas to provide a high density of critical information in an intuitive and digestible form. From machine learning basics to advanced models, each concept is presented in lay terms and then detailed precisely in mathematical form and illustrated visually. The result is a lucid, selfcontained textbook suitable for anyone with a basic background in applied mathematics.Uptodate treatment of deep learning covers cuttingedge topics not found in existing texts, such as transformers and diffusion models Short, focused chapters progress in complexity, easing students into difficult concepts Pragmatic approach straddling theory and practice gives readers the level of detail required to implement naive versions of models Streamlined presentation separates critical ideas from background context and extraneous detail Minimal mathematical prerequisites, extensive illustrations, and practice problems make challenging material widely accessible Programming exercises offered in accompanying Python Notebooks