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include runnable code, showing readers how to solve problems in practice.offer sufficient technical depth to provide a starting point on the path to actually becoming an applied machine learning scientist.Our goal is to offer a resource that could The entire book is drafted in Jupyter notebooks, seamlessly integrating exposition figures, math, and interactive examples with self-contained code.
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This open-source book represents our attempt to make deep learning approachable, teaching you the concepts, the context, and the code. D2L.ai: Interactive Deep Learning Book with Multi-Framework Code, Math, and Discussionsīook website | STAT 157 Course at UC Berkeley The best way to understand deep learning is learning by doing.