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Deep Learning with PyTorch Step-by-Step: A Beginner's Guide: Volume I: Fundamentals
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 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 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 InsideGradient descent and PyTorch’s autogradTraining loop, data loaders, mini-batches, and optimizersBinary classifiers, cross-entropy loss, and imbalanced datasetsDecision boundaries, evaluation metrics, and data separability
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 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 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 InsideGradient descent and PyTorch’s autogradTraining loop, data loaders, mini-batches, and optimizersBinary classifiers, cross-entropy loss, and imbalanced datasetsDecision boundaries, evaluation metrics, and data separability
- GenresNonfiction
282 pages, Kindle Edition
Published January 22, 2022
88 people are currently reading
66 people want to read
About the author
Daniel Voigt Godoy
5 books6 followersDaniel Voigt Godoy is an Amazon best-selling author who has self-published a series of technical books used as textbooks in universities in the United States and Spain.
In the last 25 years, he had many jobs—developer, data scientist, teacher, writer—but he's none of them. After figuring out his own path, he decided to share his experience and insights with others in his upcoming book "You're Not Your Job: Going Above and Beyond for Yourself."
In the last 25 years, he had many jobs—developer, data scientist, teacher, writer—but he's none of them. After figuring out his own path, he decided to share his experience and insights with others in his upcoming book "You're Not Your Job: Going Above and Beyond for Yourself."
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Displaying 1 of 1 review
July 10, 2023
Great intro to PyTorch. My only gripe is the non sequiturs the author likes to take you on impedes the ability to learn the core concepts at times. Also, would love to see more markdown in the complementary notebooks to make it easier to follow on the code from the readings.
Displaying 1 of 1 review