Deep Learning with PyTorch Step-by-Step: A Beginner’s Guide by Daniel Voigt Godoy
Requirements: .ePUB, .PDF, .MOBI/.AZW reader, 61mb
Overview: 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).
Genre: Non-Fiction > Educational
Download Instructions:
https://ouo.io/V7YabJv
https://ouo.io/LxoO3v
