
Deep Learning with PyTorch Step-by-Step: A Beginner's Guide - Volume I: Fundamentals
Description
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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
More details
Person
Daniel Voigt Godoy is a husband, a brother, and a son. In the last 25 years, he had many jobs ? developer, data scientist, teacher, writer ? but he's none of them. He is an avid learner and he has a curious and restless mind.
At age 46, he was finally able to switch gears. It took him several years and lots and lots of questions to figure out what was the right path for him. Now, he's finally at peace and happy with who he is while living his life the best he can.
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