Basic plain clear language. The text is not academic or arrogant, uses simple words and is easy to absorb and understand. The pace is slow and the text is not dense. There is not a ton of math notation, and the rare cases where it is included the text really helps.
I love that the author takes you through how you would build and train a neural net without Torch first. I knew how things like gradient descent worked, but this book really helped me -understand- what was happening under the hood, and now when I see my nets going sideways, I can visualize the issues. The rest of the series is the same quality I own all three now.
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From the Publisher
Is this book for me?
Daniel wrote this book for beginners in general - not only PyTorch beginners. Every now and then he will spend some time explaining some fundamental concepts which are essential to have a proper understanding of what's going on in the code.
If you have absolutely no experience with PyTorch, this is your starting point!
In this first volume of the series, you’ll be introduced to the fundamentals of PyTorch: autograd, model classes, datasets, data loaders, and more.
By the time you finish this volume, you’ll have a thorough understanding of the concepts and tools necessary to start developing and training your own models using PyTorch.
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
- ... and more!
How is this book different?
This book is written as if YOU, the reader, were having a conversation with Daniel, the author: he will ask you questions (and give you answers shortly afterward) and also make some (silly) jokes.
Moreover, this book spells concepts out in plain English, avoiding fancy mathematical notation as much as possible.
It shows you how PyTorch works, in a structured, incremental, and from-first-principles approach.
It builds, step-by-step, not only the models themselves but also your understanding as it shows you both the reasoning behind the code and how to avoid some common pitfalls and errors along the way.
"Hi, I'm Daniel!"
I am a data scientist, developer, teacher, and author of this series of books.
I will tell you, briefly, how this series of books came to be. In 2018, before teaching a class, I tried to find a blog post that would visually explain, in a clear and concise manner, the concepts behind binary cross-entropy so that I could show it to my students. Since I could not find any that fit my purpose, I decided to write one myself. It turned out to be my most popular blog post!
My readers have welcomed the simple, straightforward, and conversational way I explained the topic.
Then, in 2019, I used the same approach for writing another blog post: "Understanding PyTorch with an example: a step-by-step tutorial." Once again, I was amazed by the reaction from the readers! It was their positive feedback that motivated me to write this series of books to help beginners start their journey into deep learning and PyTorch.
I hope you enjoy reading these books as much as I enjoyed writing them!
About the author
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Top reviews from other countries
Jurij
Very good practical and beginner-friendly instructions for getting started with PyTorch
Reviewed in Germany on February 8, 2024
Amazon カスタマー
Greatest PyTorch book for everyone with a solid foundation in ML
Reviewed in Japan on June 11, 2022