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Mathematics and architecture of deep learning

49.99 €
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Mathematics and architecture of deep learning
49.99 €
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Find out what's going on inside the black box! To use deep learning, you'll need to prepare your data, select the right model, train it, evaluate its quality and accuracy, and consider handling uncertainty and variability in the output of the deployed solution. This book introduces you step-by-step to the core mathematical concepts you'll need as a data scientist—vector calculus, linear algebra, and Bayesian inference—from a deep learning perspective.
The authors explain the mathematics, theory, and principles of building deep learning models, then demonstrate how to apply this theory in practice, using Python code snippets with detailed comments. This book takes you from the fundamentals of algebra, calculus, and statistics to modern deep learning architectures, informed by the latest research.
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