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Key benefits

Compare and contrast 20+ models (including GPT-4, BERT, and Llama 2) and multiple platforms and libraries to find the right solution for your project

Apply RAG with LLMs using customized texts and embeddings

Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases

Purchase of the print or Kindle book includes a free eBook in PDF format

Description

Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, applications, and various platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV). The book guides you through different transformer architectures to the latest Foundation Models and Generative AI. You’ll pretrain and fine-tune LLMs and work through different use cases, from summarization to implementing question-answering systems with embedding-based search techniques. You will also learn the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate such risks using moderation models with rule and knowledge bases. You’ll implement Retrieval Augmented Generation (RAG) with LLMs to improve the accuracy of your models and gain greater control over LLM outputs. Dive into generative vision transformers and multimodal model architectures and build applications, such as image and video-to-text classifiers. Go further by combining different models and platforms and learning about AI agent replication. This book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.

Who is this book for?

This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning engineers, and technical leaders looking to advance their LLMs and generative AI skills or explore the latest trends in the field. Knowledge of Python and machine learning concepts is required to fully understand the use cases and code examples. However, with examples using LLM user interfaces, prompt engineering, and no-code model building, this book is great for anyone curious about the AI revolution.

What you will learn

Breakdown and understand the architectures of the Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-E

Fine-tune BERT, GPT, and PaLM 2 models

Learn about different tokenizers and the best practices for preprocessing language data

Pretrain a RoBERTa model from scratch

Implement retrieval augmented generation and rules bases to mitigate hallucinations

Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP

Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V

What do you get with Print?

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nicoleta simona Apr 05, 2024

I adore Denis Rothman's Transformers and the way things are written and explained. A 5-year-old can understand his explanation and a scientist can improve his work. When I see his interviews, I learn, on the one hand, about the ChatGPT and Google Gemini transformers and, on the other hand, how to treat clients, be a proactive human, and get a new perspective on AI. AI becomes fun, easy, and life- and perspective-changing. I read the second edition, and I can not wait to apply what I read in the 3rd edition. I mean, it is written as if it would answer the pain points of becoming a GenAI pro and maximize business and living in any circumstance. I already attended a Packt conference with Denis Rothman as a speaker in October last year. With or without his opponents, GenAI is changing the world and will make an important difference. Thank you, Denis Rothman, for Transformers for Natural Language Processing and Computer Vision. I adore it.

Subscriber review

Paul Burnett Oct 02, 2024

This book is excellently presented. I find the material and examples stimulating and which has led to a million more questions. It a very complicated area to study but this book covers the topics very well. The code works well. Great book. Many thanks. I am still digesting this fabulous book.

Feefo Verified review

v Sep 26, 2024

If you're aspiring to become an expert in NLP or Generative AI, this book is an excellent resource. It provides a clear, step-by-step explanation of NLP models, making complex concepts easy to grasp through practical examples and Python code. . Starting with foundational models, the book introduces the architecture of Transformer, BERT, and RoBERTa, followed by an in-depth exploration of the GPT models which are the Generative AI revolution. The book also delves into image processing and computer vision. Additionally, the questions at the end of each chapter further enhance understanding and engagement with the material.

Amazon Verified review

Dr. Walter Aigner Mar 15, 2024

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.

Didi Aug 01, 2024

The transformer architecture was introduced by Google in 2017, and almost instantly revolutionized the field of natural language processing (NLP), and to some degree also that of computer vision. This book is a comprehensive and practical guide to the transformer architecture, on which modern LLMs are based, and its applications in NLP and computer vision.The book does a wonderful job in providing detailed and clear descriptions of a wide range of important topics in NLP, such as the fundamentals of the transformer architecture, model pre-training, fine-tuning, tokenization, and embeddings. Notable applications of LLMs are also covered in detail, and include summarization, translation, etc. Modern generative AI methods are also very nicely covered, both in NLP and in computer vision (e.g., ChatGPT, Stable Diffusion, and the like). The accompanying GitHub repo is also very helpful, and greatly assists in reinforcing the concepts presented in the book.This comprehensive and unique guide will benefit any researcher, data scientist, machine learning engineer, or software engineer interested in building and understanding modern NLP and LLMs, as well as modern methods in computer vision. Prior familiarity with machine learning concepts, as well as with the Python programming language, would be helpful to get the most out of this book.Highly recommended!

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 , Third Edition

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