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Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide
Daniel Voigt GodoyIn 2019, I published a PyTorch tutorial on Towards Data Science and I was amazed by the reaction from the readers! Their feedback motivated me to write this book to help beginners start their journey into Deep Learning and PyTorch. I hope you enjoy reading this book as much as I enjoy writing it.
UPDATE (July, 19th, 2022): The Spanish version of Part I, Fundamentals, was published today:https://leanpub.com/pytorch_ES
UPDATE (February 23rd, 2022): The paperback edition is available now (the book had to be split into 3
volumes for printing). For more details, please check pytorchstepbystep.com.
UPDATE (February 13th, 2022): The latest revised edition (v1.1.1) was published today to address small changes to Chapters 9 and 10 that weren't included in the previous revision.
UPDATE (January 23rd, 2022): The revised edition (v1.1) was published today - better graphics, improved formatting, larger page size (thus reducing page count from 1187 to 1045 pages - no content was removed!). If you already bought the book, you can download the new version at any time!
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,
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