Math is essential to the modern world. Deep learning is also rapidly becoming essential. From the promise of self-driving cars to medical systems detecting fractures better than all but the very best physicians, to say nothing of increasingly capable, and possibly worrisome, voice-controlled assistants, deep learning is everywhere.

This book covers the essential math for making deep learning comprehensible. It’s true that you can learn the toolkits, set up the configuration files or Python code, format some data, and train a model, all without understanding what you’re doing, let alone the math behind it. And, because of the power ...

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