Deep Learning with PyTorch Step-by-Step - Volume III: Sequences & NLP

THIS BOOK IS AVAILABLE AT MY NEW PAGE:

https://danielgodoy.gumroad.com/l/pytorch3

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!

Is this book for me?

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 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.

How is this book different?

• I wrote this book as if I were having a conversation with YOU, the reader: I will ask you questions (and give you answers shortly afterward) and I will also make (silly) jokes.
• It spells concepts out in plain English, avoiding fancy mathematical notation as much as possible
• It shows you how to use PyTorch for sequences and NLP tasks, in a structured, incremental, and from first principles approach
• It builds, step-by-step, not only the models themselves but also your understanding as I show you both the reasoning behind the code and how to avoid some common pitfalls and errors along the way.

What if I do not like the book? Can I get a refund?

Yes, you can! No questions asked! If you are not happy with your purchase, just reply to the download email within 30 days, and you will get your money back.

Can I share the book with my classmates?

If you'd like to share the book within your classmates, please choose the Classmates version on checkout.

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

Table of Contents

• Chapter 8: Sequences
• Chapter 9: Sequence-to-Sequence
• Chapter 10: Transform and Roll Out
• Chapter 11: Down the Yellow Brick Rabbit Hole

Testimonials

"I am usually really picky in choosing books about ML/DL but I have to tell you, this book was one of the best books I have ever invested in. I cannot thank you enough for writing a book that gives so much clarity on the explanations of the inner workings of many DL techniques. Thank you so much and I hope you come up with even better books on other ML topics in the future."

— Mahmud Hasan, Machine Learning Engineer at Micron Technology, Smart Manufacturing and AI

"As an author myself who've co-authored two books in Deep Learning & NLP space, I'm extremely impressed by Daniel's step-by-step pedagogical approach. Starting with a toy problem and gradually building abstractions on top of each other massively helps beginner to understand the nuts and bolts of each models and neural architectures be it basic or advanced! Daniel has justified "step-by-step" part from the title in a true sense. Highly recommended!"

— Nipun Nayan Sadvilkar, Lead Data Scientist & Author, DL & NLP Workshop

"We love the book as it’s so easy to read. The author uses simple words and avoids complex mathematical formulas, making the text feel like a conversation between friends."

— Zuzanna Sieja from DLabs.AI on 11 Books Every Data Scientist Must Read In 2022

"As for learning PyTorch and deep learning in general, Deep Learning with PyTorch Step-by-Step by Daniel Voigt Godoy is easily the best guide that I’ve found. I love how this huge hands-on tutorial it is structured, it starts from the ground level, then after showing the basic things, it goes straight into computer vision topics and in the end you get to know transformers and word embeddings, all of which play important part in the inner workings of CLIP."

— johanezz from deeplearn.art on Get started with making AI art in 2022

"This looks like it is very comprehensive, rich in detail and it might have been a huge investment of time and effort to write this. Kudos to you for making the convoluted stuff so enjoyable to read and easy to understand."

— Rishabh Kumar, Business Intelligence Analyst, on Linkedin

"This is the best book I have read in my entire life, in terms of richness, intuition, and practicality (also your puns)."

— @Teddy2kay1 on Twitter