Description
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
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