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.
Learn how to pretrain and fine-tune LLMs
Learn how to work with multiple platforms, such as Hugging Face, OpenAI, and Google Vertex AI
Learn about different tokenizers and the best practices for preprocessing language data
Implement Retrieval Augmented Generation and rules bases to mitigate hallucinations
Visualize transformer model activity for deeper insights using BertViz, LIME, and SHAP
Create and implement cross-platform chained models, such as HuggingGPT
Go in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V
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.