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Full Description
The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal AI, risk mitigation, and practical implementations with ChatGPT, Hugging Face, and Vertex AI
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Key Features
Compare and contrast 20+ models (including GPT, BERT, and Llama) 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
Book DescriptionTransformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Model (LLM) architectures, practical applications, and popular platforms (Hugging Face, OpenAI, and Google Vertex AI) used for Natural Language Processing (NLP) and Computer Vision (CV).
The book guides you through a range of transformer architectures from foundation models and generative AI. You'll pretrain and fine-tune LLMs and work through different use cases, from summarization to question-answering systems leveraging embedding-based search. You'll also implement Retrieval Augmented Generation (RAG) to enhance accuracy and gain greater control over your LLM outputs. Additionally, you'll understand common LLM risks, such as hallucinations, memorization, and privacy issues, and implement mitigation strategies using moderation models alongside rule-based systems and knowledge integration.
Dive into generative vision transformers and multimodal architectures, and build practical applications, such as image and video classification. Go further and combine different models and platforms to build AI solutions and explore AI agent capabilities.
This book provides you with an understanding of transformer architectures, including strategies for pretraining, fine-tuning, and LLM best practices.What you will learn
Breakdown and understand the architectures of the Transformer, BERT, GPT, T5, PaLM, ViT, CLIP, and DALL-E
Fine-tune BERT, GPT, and PaLM 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, and GPT
Who this book is forThis book is ideal for NLP and CV engineers, data scientists, machine learning practitioners, software developers, and technical leaders looking to advance their expertise in LLMs and generative AI or explore latest industry trends.
Familiarity with Python and basic machine learning concepts will help you fully understand the use cases and code examples. However, hands-on examples involving LLM user interfaces, prompt engineering, and no-code model building ensure this book remains accessible to anyone curious about the AI revolution.
Contents
Table of Contents
What are Transformers?
Getting Started with the Architecture of the Transformer Model
Emergent vs Downstream Tasks: The Unseen Depths of Transformers
Advancements in Translations with Google Trax, Google Translate, and Gemini
Diving into Fine-Tuning through BERT
Pretraining a Transformer from Scratch through RoBERTa
The Generative AI Revolution with ChatGPT
Fine-Tuning OpenAI GPT Models
Shattering the Black Box with Interpretable Tools
Investigating the Role of Tokenizers in Shaping Transformer Models
Leveraging LLM Embeddings as an Alternative to Fine-Tuning
Toward Syntax-Free Semantic Role Labeling with ChatGPT and GPT-4
Summarization with T5 and ChatGPT
Exploring Cutting-Edge LLMs with Vertex AI and PaLM 2
Guarding the Giants: Mitigating Risks in Large Language Models
Beyond Text: Vision Transformers in the Dawn of Revolutionary AI
Transcending the Image-Text Boundary with Stable Diffusion
Hugging Face AutoTrain: Training Vision Models without Coding
On the Road to Functional AGI with HuggingGPT and its Peers
Beyond Human-Designed Prompts with Generative Ideation
