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

The role of AI professionals

Transformer-driven AI is connecting everything to everything, everywhere. Machines communicate directly with other machines. AI-driven IoT signals trigger automated decisions without human intervention. NLP algorithms send automated reports, summaries, emails, advertisements, and more.

AI specialists must adapt to this new era of increasingly automated tasks, including transformer model implementations. AI professionals will have new functions. If we list transformer NLP tasks that an AI specialist will have to do, from top to bottom, it appears that some high-level tasks require little to no development from an AI specialist. An AI specialist can be an AI guru, providing design ideas, explanations, and implementations.

The pragmatic definition of what a transformer represents for an AI specialist will vary with the ecosystem.

Let’s go through a few examples:

API: The OpenAI API does not require an AI developer. A web designer...

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

Table of Contents (24) Chapters

The role of AI professionals

Authors (1)

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