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Bibliographic Details

Title: Deep Learning with PyTorch Step-by-Step: A Beginner's Guide: Volume I: Fundamentals
Publisher: Unknown
Publication Date: 2022
Language: English
ISBN 13: 9798533935746
Binding: paperback
Condition: Very Good

About this title

Synopsis

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

"About this title" may belong to another edition of this title.

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