Deep Learning with PyTorch Step-by-Step: A Beginner's Guide - Volume III: Sequences & NLP. Deep Learning with PyTorch Step-by-Step: A Beginner's Guide, #3
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- FormatePub
- ISBN8230150244
- EAN9798230150244
- Date de parution18/02/2025
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurIndependently Published
Résumé
Revised for PyTorch 2.x!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!How is this book different? First, this book presents an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch.
Second, this is a rather informal book: It is written as if you, the reader, were having a conversation with Daniel, the author. His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and spells everything out in plain English. 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 Hugging Face 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. 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'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
Second, this is a rather informal book: It is written as if you, the reader, were having a conversation with Daniel, the author. His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and spells everything out in plain English. 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 Hugging Face 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. 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'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
Revised for PyTorch 2.x!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!How is this book different? First, this book presents an easy-to-follow, structured, incremental, and from-first-principles approach to learning PyTorch.
Second, this is a rather informal book: It is written as if you, the reader, were having a conversation with Daniel, the author. His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and spells everything out in plain English. 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 Hugging Face 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. 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'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
Second, this is a rather informal book: It is written as if you, the reader, were having a conversation with Daniel, the author. His job is to make you understand the topic well, so he avoids fancy mathematical notation as much as possible and spells everything out in plain English. 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 Hugging Face 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. 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'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
