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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
Concepts
Author (1):
Denis Rothman Denis Rothman
Profile icon Denis Rothman
LinkedIn
Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.
See other products by 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

How constant time complexity O(1) changed our lives forever

How could this deceivingly simple O(1) time complexity class forever change AI and our everyday lives? How could O(1) explain the profound architectural changes that made ChatGPT so powerful and stunned the world? How can something as simple as O(1) allow systems like ChatGPT to spread to every domain and hundreds of tasks?

The answer to these questions is the only way to find your way in the growing maze of transformer datasets, models, and applications is to focus on the underlying concepts of thousands of assets. Those concepts will take you to the core of the functionality you need for your projects.

This section will provide a significant answer to those questions before we move on to see how one token (a minimal piece of a word) started an AI revolution that is raging around the world, triggering automation never seen before.

We need to get to the bottom of the chaos and disruption generated by transformers...

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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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Authors (1)

Profile icon Denis Rothman
LinkedIn
Denis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide.
See other products by Denis Rothman

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