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Transformers for Natural Language Processing and Computer Vision - Third Edition

You're reading from  Transformers for Natural Language Processing and Computer Vision - Third Edition

Product type Book
Published in Feb 2024
Publisher Packt
ISBN-13 9781805128724
Pages 728 pages
Edition 3rd Edition
Languages
Author (1):
Denis Rothman Denis Rothman
Profile icon Denis Rothman

Table of Contents (24) Chapters

Preface What Are Transformers? Getting Started with the Architecture of the Transformer Model Emergent vs Downstream Tasks: The Unseen Depths of Transformers Advancements in Translations with Google Trax, Google Translate, and Gemini Diving into Fine-Tuning through BERT Pretraining a Transformer from Scratch through RoBERTa The Generative AI Revolution with ChatGPT Fine-Tuning OpenAI GPT Models Shattering the Black Box with Interpretable Tools Investigating the Role of Tokenizers in Shaping Transformer Models Leveraging LLM Embeddings as an Alternative to Fine-Tuning Toward Syntax-Free Semantic Role Labeling with ChatGPT and GPT-4 Summarization with T5 and ChatGPT Exploring Cutting-Edge LLMs with Vertex AI and PaLM 2 Guarding the Giants: Mitigating Risks in Large Language Models Beyond Text: Vision Transformers in the Dawn of Revolutionary AI Transcending the Image-Text Boundary with Stable Diffusion Hugging Face AutoTrain: Training Vision Models without Coding On the Road to Functional AGI with HuggingGPT and its Peers Beyond Human-Designed Prompts with Generative Ideation Other Books You May Enjoy
Index
Appendix: Answers to the Questions

Who this book is for

This book is not an introduction to Python programming or machine learning concepts. Instead, it focuses on deep learning for machine translation, speech-to-text, text-to-speech, language modeling, question answering, and many more NLP domains, as well as computer vision multimodal tasks.

Readers who can benefit the most from this book are:

  • Deep learning, vision, and NLP practitioners familiar with Python programming.
  • Data analysts, data scientists, and machine learning/AI engineers who want to understand how to process and interrogate the increasing amounts of language-driven and image data.
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