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

By: Daniel Voigt Godoy

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 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. 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, 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 Inside Gradient descent and PyTorch's autograd Training loop, data loaders, mini-batches, and optimizers Binary classifiers, cross-entropy loss, and imbalanced datasets Decision boundaries, evaluation metrics, and data separability

• Format: Paperback
• Language: English
• ISBN: B09QR4M768
• ISBN13: 9798533935746
• Release Date: January 2022
• Publisher: Independently published
• Length: 281 Pages
• Weight: 1.21 lbs.
• Dimensions: 10.0" x 0.6" x 7.0"