Unleash the full potential of transformers with this comprehensive guide covering architecture, capabilities, risks, and practical implementations on OpenAI, Google Vertex AI, and Hugging Face
Key Features: Master NLP and vision transformers, from the architecture to fine-tuning and implementation Learn how to apply Retrieval Augmented Generation (RAG) with LLMs using customized texts and embeddings Mitigate LLM risks, such as hallucinations, using moderation models and knowledge bases
Book Description: Transformers for Natural Language Processing and Computer Vision, Third Edition, explores Large Language Models' (LLMs) 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. This book explains the risks of LLMs, from hallucinations and memorization to privacy, and how to mitigate 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 give you 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.
What You Will Learn: 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
Who this book is for: This book is ideal for NLP and CV engineers, software developers, data scientists, machine learning