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Discover what's going on inside the black box! To work with deep learning you'll have to choose the right model, train it, preprocess your data, evaluate performance and accuracy, and deal with uncertainty and variability in the outputs of a deployed solution. This book takes you systematically through the core mathematical concepts, linear algebra, and Bayesian inference, all from a deep learning perspective. Math and archtectures of deep learning teaches the math, theory, and programming principles of deep learning models laid out side by side, and then puts them into practice with well-annotated Python code. You'll progrress from algebra, calculus, and statistics all the way to state-of-the-art DL architectures taken from the latest research

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