An authoritative accessible and up-to-date treatment of deep learning that strikes a
pragmatic middle ground between theory and practice. Deep learning is a fast-moving field
with sweeping relevance in today’s increasingly digital world. Understanding Deep Learning
provides an authoritative accessible and up-to-date treatment of the subject covering all
the key topics along with recent advances and cutting-edge 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 self-contained textbook suitable for anyone with a basic
background in applied mathematics. Up-to-date treatment of deep learning covers cutting-edge
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
