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

Summary

In this chapter, we first defined generative ideation. The core concept resides in one sentence:

AI has gone from executing tasks based on human prompts to automating ideation enhancing human thinking and imagination in an ethical ecosystem.

Generative ideation can be constructive and implemented without destroying human jobs. For example, generative ideation can help small businesses with no marketing resources to access human-level AI services to compete with larger entities.

We then demonstrated that generative text-to-image ideation was possible with ChatGPT/GPT-4, Llama 2, Midjourney, and Microsoft Designer.

Finally, we automated the generative text-to-image ideation pipeline with automated prompt design, ChatGPT/GPT-4, and Stable Diffusion. We saw that you could use the multiple tools explored in this book to build an effective ecosystem.

We also peeked into the future of automation and AI productivity.

You can leverage automated human thinking...