Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide: Volume III: Sequences & NLP

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(as of Dec 16,2024 05:51:48 UTC – Details)

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

Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide: Volume III: Sequences & NLP

In this third volume of our beginner’s guide to deep learning with PyTorch, we will dive into the world of sequences and natural language processing (NLP). Sequences are a fundamental data structure in many applications, such as time series data, text data, and more. NLP, on the other hand, deals with the processing and understanding of human language using computational techniques.

In this guide, we will cover the following topics:

1. Introduction to Sequences: We will start by understanding what sequences are and why they are important in deep learning. We will explore different types of sequences, such as time series data and text data.

2. Sequence Models with PyTorch: We will learn how to build sequence models using PyTorch, including recurrent neural networks (RNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRUs).

3. Natural Language Processing (NLP) Basics: We will introduce the basic concepts of NLP, such as tokenization, word embeddings, and text classification.

4. Building NLP Models with PyTorch: We will explore how to build NLP models using PyTorch, including text classification, sentiment analysis, and named entity recognition.

Throughout this guide, we will provide step-by-step instructions and code examples to help you understand and implement these concepts in PyTorch. By the end of this volume, you will have a solid understanding of how to work with sequences and NLP using PyTorch, and you will be ready to tackle more advanced deep learning tasks in these domains. Stay tuned for more updates and happy learning!
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