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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 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

From one token to an AI revolution

Yes, the title is correct, as you will see in this section. One token produced an AI revolution and has opened the door to AI in every domain and application.

ChatGPT with GPT-4, PaLM 2, and other LLMs have a unique way of producing text.

In LLMs, a token is a minimal word part. The token is where a Large Language Model starts and ends.

For example, the word including could become: includ + ing, representing two tokens. GPT models predict tokens based on the hundreds of billions of tokens in its training dataset. Examine the graph in Figure 1.9 of an OpenAI GPT model that is making an inference to produce a token:

A diagram of a diagram  Description automatically generated

Figure 1.9: GPT inference graph built in Python with NetworkX

It may come as a surprise, but the only parts of this figure controlled by the model are Model and Output Generation!, which produce raw logits. All the rest is in the pipeline.

To understand the pipeline, we will first go through the description...

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Transformers for Natural Language Processing and Computer Vision - Third Edition
Published in: Feb 2024 Publisher: Packt ISBN-13: 9781805128724
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