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Daniel Voigt
Deep Learning with PyTorch Step-by-Step: A Beginners Guide - Volume I: Fundamentals
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7304357Deep Learning with PyTorch Step-by-Step: A Beginners Guide - Volume I: Fundamentalshttps://www.gandhi.com.mx/deep-learning-with-pytorch-step-by-step--a-beginners-guide---volume-i--fundamentals-9798230900696/phttps://gandhi.vtexassets.com/arquivos/ids/6872361/image.jpg?v=6387515532430300008181MXNGandhiInStock/Ebooks/<p><strong>Revised for PyTorch 2.x!</strong></p><p><strong>Why this book?</strong></p><p>Are you looking for a book where you can <strong>learn about deep learning and PyTorch</strong> without having to spend hours deciphering cryptic text and code? A technical book thats also <strong>easy and enjoyable to read</strong>?</p><p><strong>This is it!</strong></p><p><strong>How is this book different?</strong></p><ul><li>First, this book presents an <strong>easy-to-follow</strong>, <strong>structured</strong>, <strong>incremental</strong>, and <strong>from-first-principles</strong> approach to learning PyTorch.</li><li>Second, this is a rather informal book: It is written <strong>as if you, the reader, were having a conversation with Daniel, the author</strong>.</li><li>His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and <strong>spells everything out in plain English</strong>.</li></ul><p><strong>What will I learn?</strong></p><p>In this first volume of the series, youll be introduced to the fundamentals of PyTorch: <strong>autograd</strong>, model classes, <strong>datasets</strong>, <strong>data loaders</strong>, and more. You will develop, step-by-step, not only the models themselves but also your understanding of them.</p><p>By the time you finish this book, youll have a thorough understanding of the concepts and tools necessary to <strong>start developing and training your own models using PyTorch</strong>.</p><p><strong>If you have absolutely no experience with PyTorch, this is your starting point.</strong></p><p><strong>Whats Inside</strong></p><ul><li>Gradient descent and PyTorchs autograd</li><li>Training loop, data loaders, mini-batches, and optimizers</li><li>Binary classifiers, cross-entropy loss, and imbalanced datasets</li><li>Decision boundaries, evaluation metrics, and data separability</li></ul>...6937345Deep Learning with PyTorch Step-by-Step: A Beginners Guide - Volume I: Fundamentals8181https://www.gandhi.com.mx/deep-learning-with-pytorch-step-by-step--a-beginners-guide---volume-i--fundamentals-9798230900696/phttps://gandhi.vtexassets.com/arquivos/ids/6872361/image.jpg?v=638751553243030000InStockMXN99999DIEbook20259798230900696_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_<p><strong>Revised for PyTorch 2.x!</strong></p><p><strong>Why this book?</strong></p><p>Are you looking for a book where you can <strong>learn about deep learning and PyTorch</strong> without having to spend hours deciphering cryptic text and code? A technical book thats also <strong>easy and enjoyable to read</strong>?</p><p><strong>This is it!</strong></p><p><strong>How is this book different?</strong></p><ul><li>First, this book presents an <strong>easy-to-follow</strong>, <strong>structured</strong>, <strong>incremental</strong>, and <strong>from-first-principles</strong> approach to learning PyTorch.</li><li>Second, this is a rather informal book: It is written <strong>as if you, the reader, were having a conversation with Daniel, the author</strong>.</li><li>His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and <strong>spells everything out in plain English</strong>.</li></ul><p><strong>What will I learn?</strong></p><p>In this first volume of the series, youll be introduced to the fundamentals of PyTorch: <strong>autograd</strong>, model classes, <strong>datasets</strong>, <strong>data loaders</strong>, and more. You will develop, step-by-step, not only the models themselves but also your understanding of them.</p><p>By the time you finish this book, youll have a thorough understanding of the concepts and tools necessary to <strong>start developing and training your own models using PyTorch</strong>.</p><p><strong>If you have absolutely no experience with PyTorch, this is your starting point.</strong></p><p><strong>Whats Inside</strong></p><ul><li>Gradient descent and PyTorchs autograd</li><li>Training loop, data loaders, mini-batches, and optimizers</li><li>Binary classifiers, cross-entropy loss, and imbalanced datasets</li><li>Decision boundaries, evaluation metrics, and data separability</li></ul>...9798230900696_Daniel Voigt Godoylibro_electonico_9798230900696_9798230900696Daniel VoigtInglésMéxico2025-02-18T00:00:00+00:00https://getbook.kobo.com/koboid-prod-public/draft2digital_ipp-epub-865ca51b-3b2a-46d6-8e4e-af8c85764fcd.epub2025-02-18T00:00:00+00:00Daniel Voigt Godoy