Godoy D.V. - Deep Learning with PyTorch [2024, PDF, ENG] torrent

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Deep Learning with PyTorch

Год издания: 2024
Автор: Godoy D.V.

Издательство: Leanpub
ISBN: отсутствует
Язык: Английский

Формат: PDF
Качество: Издательский макет или текст (eBook)
Интерактивное оглавление: Да
Количество страниц: 1047

Описание: If you're looking for a book where you can learn about Deep Learning and PyTorch without having to spend hours deciphering cryptic text and code, and that's easy and enjoyable to read, this is it
The book covers from the basics of gradient descent all the way up to fine-tuning large NLP models (BERT and GPT-2) using HuggingFace. It is divided into four parts:
Part I: Fundamentals (gradient descent, training linear and logistic regressions in PyTorch)
Part II: Computer Vision (deeper models and activation functions, convolutions, transfer learning, initialization schemes)
Part III: Sequences (RNN, GRU, LSTM, seq2seq models, attention, self-attention, transformers)
Part IV: Natural Language Processing (tokenization, embeddings, contextual word embeddings, ELMo, BERT, GPT-2)
This is not a typical book: most tutorials start with some nice and pretty image classification problem to illustrate how to use PyTorch. It may seem cool, but I believe it distracts you from the main goal: how PyTorch works? In this book, I present a structured, incremental, and from first principles approach to learn PyTorch (and get to the pretty image classification problem in due time).
PyTorch is the fastest-growing framework for developing Deep Learning models and it has a huge ecosystem. That is, there are many tools and libraries developed on top of PyTorch. It is the preferred framework in academia already and is making its way in the industry. This book aims to get you started with PyTorch while giving you a solid understanding of how it works.
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[spoiler="Оглавление"]
Preface xxiii
Acknowledgements xxiv
About the Author xxv
Frequently Asked Questions (FAQ) 1
Setup Guide 8
Part I: Fundamentals 21
Chapter 0: Visualizing Gradient Descent 22
Chapter 1: A Simple Regression Problem 60
Chapter 2: Rethinking the Training Loop 123
Chapter 2.1: Going Classy 176
Chapter 3: A Simple Classification Problem 207
Part II: Computer Vision 265
Chapter 4: Classifying Images 266
Chapter 5: Convolutions 346
Chapter 6: Rock, Paper, Scissors 418
Chapter 7: Transfer Learning 500
Part III: Sequences 588
Chapter 8: Sequences 589
Chapter 9 — Part I: Sequence-to-Sequence 688
Chapter 9 — Part II: Sequence-to-Sequence 742
Chapter 10: Transform and Roll Out 798
Part IV: Natural Language Processing 881
Chapter 11: Down the Yellow Brick Rabbit Hole 882
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