Price: $9.99
(as of Oct 14, 2024 07:26:50 UTC – Details)
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 HuggingFace 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 InsideRecurrent neural networks (RNN, GRU, and LSTM) and 1D convolutionsSeq2Seq models, attention, masks, and positional encodingTransformers, layer normalization, and the Vision Transformer (ViT)BERT, GPT-2, word embeddings, and the HuggingFace library
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.
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.
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 HuggingFace 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.
What’s inside Recurrent neural networks (RNN, GRU, and LSTM) and 1D convolutions Seq2Seq models, attention, self-attention, masks, and positional encoding Transformers, layer normalization, and the Vision Transformer (ViT) BERT, GPT-2, word embeddings, and the HuggingFace library … 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 the inner workings of sequence models, 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!
ASIN : B09R144VB5
Publisher : Self-Published (January 22, 2022)
Publication date : January 22, 2022
Language : English
File size : 29006 KB
Simultaneous device usage : Unlimited
Text-to-Speech : Enabled
Screen Reader : Supported
Enhanced typesetting : Enabled
X-Ray : Not Enabled
Word Wise : Not Enabled
Print length : 682 pages
