Are
you looking for a book with which 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!

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.

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.

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