Deep Learning with PyTorch Step-by-Step: A Beginner's Guide - Volume I: Fundamentals
Deep Learning with PyTorch Step-by-Step: A Beginner's Guide, #1
Sinopsis
Revised for PyTorch 2.x!
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 thats 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. 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.What will I learn?
In this first volume of the series, youll 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, youll 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.
Whats Inside
Gradient descent and PyTorchs autograd Training loop, data loaders, mini-batches, and optimizers Binary classifiers, cross-entropy loss, and imbalanced datasets Decision boundaries, evaluation metrics, and data separability
Léelo en cualquier dispositivo
Ficha Técnica
Editorial: Daniel Voigt Godoy
ISBN: 9798230900696
Idioma: Inglés
Fecha de lanzamiento: 18/02/2025
Especificaciones del producto
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