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Description of  Deep Learning with PyTorch Step-by-Step: A Beginner's Guide: Volume I: Fundamentals

B09R144ZC2 pdf Why this book?Are you looking for a book where you can learn about deep learning and PyTorch without having to spend hours deciphering cryptic text and code? A technical book that's also easy and enjoyable to read?This is it!How is this book different?First, this book presents an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch.First, this book presents an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch.Second, this is a rather informal book: It is written as if you, the reader, were having a conversation with Daniel, the author.Second, this is a rather informal book: It is written as if you, the reader, were having a conversation with Daniel, the author.His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and spells everything out in plain English.His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and spells everything out in plain English.What will I learn?In this first volume of the series, you'll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more. You will develop, step-by-step, not only the models themselves but also your understanding of them.By the time you finish this book, you'll have a thorough understanding of the concepts and tools necessary to start developing and training your own models using PyTorch.If you have absolutely no experience with PyTorch, this is your starting point.What's InsideGradient descent and PyTorch's autogradGradient descent and PyTorch's autogradTraining loop, data loaders, mini-batches, and optimizersTraining loop, data loaders, mini-batches, and optimizersBinary classifiers, cross-entropy loss, and imbalanced datasetsBinary classifiers, cross-entropy loss, and imbalanced datasetsDecision boundaries, evaluation metrics, and data separabilityDecision boundaries, evaluation metrics, and data separability