Transformers for Natural Language Processing and Computer Vision: Explore Generative AI and Large Language Models with Hugging Face, ChatGPT, GPT-4V, and DALL-E 3 3rd Edition

for those who can read, I can definitely say that this new third edition provides a fresh look at both the transformers themselves and the current environment in which they exist.A valuable resource to refresh our knowledge and inspire us to take the next stepsmy personal selection of what I appreciated in this third edition after about ten days of perusing, reading and note-takingthe emergence of new roles:* The role of AI professionals* The future of AI professionals* What resources should we use?* Guidelines for decision making* Chapter 3: Emergent vs. downstream tasks: The Unseen Depths of Transformers* Chapter 7: The Generative AI Revolution with ChatGPT* Chapter 12: Towards Syntax-Free Semantic Role Labelling with ChatGPT and GPT-4* Chapter 16: Beyond Text: Vision Transformers at the Dawn of Revolutionary AIRothman writes that this book is for data analysts, data scientists, and machine learning/AI engineers who want to understand how to process and interrogate the increasing amounts of speech and image data. Most of the programs in the book are Colaboratory notebooks. All you need is a free Google Gmail account and you can run the notebooks on the free Google Colaboratory VM.Context of my interest in this field: Shortly after the public release of ChatGPT in November 2022, Bill Gates described it and other LLMs as "as important as the PC, as important as the Internet". Jensen Huang, CEO of Nvidia, said ChatGPT was "truly one of the greatest things ever done for computing". Geoffrey Hinton, a Turing Laureate, said, "I think it's comparable in scale to the industrial revolution or electricity - or maybe the wheel. Perhaps that is why many of us need a qualified, updated context.I can definitely say that this new third edition gives a qualified context and fresh look at both the transformers themselves and the current environment in which they exist.and yet, the term "Computer Simulation" is far more accurate as an umbrella term than any characterization of machine software("AI," "LLM," "Generative AI," etc.).Rothman's profile shows that he has been designing and developing computer simulation software for decades in various forms: rule-based, expert systems, ML agents, DL agents, the first transformer models, and now trending Generative AI for NLP and Computer Vision. all these algorithms boil down to "computer simulation", no more, no less. They are toolss that are here for us to make "simulations" to enhance our abilities as a scientific calculator does.Who this book is for: Anyone who regularly works with LLMs professionally (e.g. data scientists, machine learning engineers, AI researchers, etc.) or anyone already familiar with natural language processing (NLP) who wants to take a deep dive into transformers.Another reviewer rightly wrote: Who this book is not for: Anyone with little to no knowledge of NLP, machine learning, or Python programming (i.e. the "casual" reader). This book is dense (in the sense of Clifford Geertz‘ thick description that helps us increase our understanding on both on a theoretical and a practical level). I still have a lot to think about.And I have to admit that I have not yet fully grasped all the emerging possibilities and food for thought that the book has triggered or will trigger as I re-read and explore the code provided.

This book invites you on a journey to unlock the full potential of transformer architectures. From foundational understanding to advanced implementations, this comprehensive guide covers architecture, capabilities, risks, and practical applications on platforms such as OpenAI, Google Vertex AI, and Hugging Face. The book introduces readers to cutting-edge concepts like Retrieval Augmented Generation (RAG) with Large Language Models (LLMs) and provides strategies to mitigate risks associated with LLMs, such as hallucinations, using moderation models and knowledge bases.With a focus on hands-on learning, the book walks readers through pretraining and fine-tuning LLMs, working with various platforms, implementing retrieval augmented generation techniques, and visualizing transformer model activities for deeper insights. Moreover, it delves into generative vision transformers and multimodal model architectures, empowering readers to build applications ranging from image and video-to-text classifiers to AI agent replication. The book caters to NLP and CV engineers, software developers, data scientists, and machine learning enthusiasts, providing both foundational knowledge and advanced techniques to elevate their skills in the transformative field of generative AI. Whether you're an expert seeking to stay abreast of the latest trends or a novice eager to embark on the AI revolution, this book serves as an indispensable resource for navigating the intricacies of transformer models in NLP and computer vision.

A must-read for anyone interested in the cutting edge of AI. This latest edition introduces something new to the table, including updates on computer vision and the application of transformers beyond text analysis.Denis excels at making complex concepts both understandable and engaging. Exploring advancements such as PaLM 2, Llama 2, and the application of Retrieval Augmented Generation (RAG), along with insights into platforms like Hugging Face and Google Vertex AI, felt like receiving direct guidance from Rothman through the intricacies of these technologies. This book goes beyond theoretical exploration, providing actionable steps for utilizing these tools in real-world scenarios. Such an approach not only deepens our comprehension but also enhances our ability to effectively control and refine AI models across various platforms.Having read the first two editions, I appreciate how this book builds on the basics while introducing new concepts like computer vision and multimodal models. Rothman has a way of making you feel like an expert, even if you're not.The book also addresses the ethical side of AI, discussing how to manage risks associated with large language models. This is crucial for anyone looking to use AI responsibly in their work.In summary, Rothman’s third edition is a comprehensive yet approachable guide that builds on the previous editions, making the complex world of AI more accessible to everyone. Whether you're a seasoned professional or new to the field, this book has something for you.