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 third volume of the series, you'll be introduced to all things sequence-related: recurrent neural networks and their variations, sequence-to-sequence models, attention, self-attention, and Transformers.

This volume also includes a crash course on natural language processing (NLP), from the basics of word tokenization all the way up to fine-tuning large models (BERT and GPT-2) using the Hugging Face library.

By the time you finish this book, you'll have a thorough understanding of the concepts and tools necessary to start developing, training, and fine-tuning language models using PyTorch.

This volume is more demanding than the other two, and you're going to enjoy it more if you already have a solid understanding of deep learning models.

What's Inside

• Recurrent neural networks (RNN, GRU, and LSTM) and 1D convolutions


• Seq2Seq models, attention, masks, and positional encoding


• Transformers, layer normalization, and the Vision Transformer (ViT)


• BERT, GPT-2, word embeddings, and the HuggingFace library