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

Python

Concepts

Deep Learning

Author (1):

Denis Rothman

Table of Contents (24) Chapters

Preface

1. What Are Transformers?

2. Getting Started with the Architecture of the Transformer Model

3. Emergent vs Downstream Tasks: The Unseen Depths of Transformers

4. Advancements in Translations with Google Trax, Google Translate, and Gemini

5. Diving into Fine-Tuning through BERT

6. Pretraining a Transformer from Scratch through RoBERTa

7. The Generative AI Revolution with ChatGPT

8. Fine-Tuning OpenAI GPT Models

9. Shattering the Black Box with Interpretable Tools

10. Investigating the Role of Tokenizers in Shaping Transformer Models

11. Leveraging LLM Embeddings as an Alternative to Fine-Tuning

12. Toward Syntax-Free Semantic Role Labeling with ChatGPT and GPT-4

13. Summarization with T5 and ChatGPT

14. Exploring Cutting-Edge LLMs with Vertex AI and PaLM 2

15. Guarding the Giants: Mitigating Risks in Large Language Models

16. Beyond Text: Vision Transformers in the Dawn of Revolutionary AI

17. Transcending the Image-Text Boundary with Stable Diffusion

18. Hugging Face AutoTrain: Training Vision Models without Coding

19. On the Road to Functional AGI with HuggingGPT and its Peers

20. Beyond Human-Designed Prompts with Generative Ideation

21. Other Books You May Enjoy

22. Index

Appendix: Answers to the Questions

Architecture

PaLM and PaLM2 were built on top of Pathways. Pathways is a Google technology that improves the efficiency of training LLMs through data parallelism, model parallelism, and execution-level parallelism.

We will begin with Pathways, the cornerstone of Google AI’s impressive achievements.

Pathways

The title of the Pathways paper may seem esoteric. Pathways: Asynchronous Distributed Dataflow by Barham et al. (2022) indeed appears like something you might want to avoid looking into. However, once you start reading the paper, you will be hooked!

If we look at some of the key features, we are somewhat stunned:

Heterogeneous execution: Pathways can run programs on many devices, including TPUs, CPUs, and GPUs. This is a significant advance when assembling all the computing power we can get.
Asynchronous execution: Pathways allows programs to be executed asynchronously. This might seem uninteresting, but PaLM will build on this technology...