Bøger af Denis Rothman

The definitive guide to LLMs, from architectures, pretraining, and fine-tuning to Retrieval Augmented Generation (RAG), multimodal Generative AI, risks, and implementations with ChatGPT Plus with GPT-4, Hugging Face, and Vertex AIKey Features:Compare and contrast 20+ models (including GPT-4, BERT, and Llama 2) and multiple platforms and libraries to find the right solution for your projectApply RAG with LLMs using customized texts and embeddingsMitigate LLM risks, such as hallucinations, using moderation models and knowledge basesPurchase of the print or Kindle book includes a free eBook in PDF formatBook 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 and Computer Vision. The book guides you through different transformer architectures to 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 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, 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:Breakdown and understand the architectures of Original Transformer, BERT, GPT models, T5, PaLM, ViT, CLIP, and DALL-EFine-tune BERT, GPT, and PaLM 2 modelsLearn about different tokenizers and best practices for preprocessing language dataPretrain a RoBERTa model from scratchImplement retrieval augmented generation and rules bases to mitigate hallucinationsVisualize transformer model activity for deeper insights using BertViz, LIME, and SHAPGo in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4VWho this book is 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, no-code model building, this book is great for anyone curious about AI revolution.

In today's era of AI, accurately interpreting and communicating trustworthy, fair, and secure AI findings have become a crucial skill to master. This book bridges the gap between AI's pitfalls and potential by helping you build the ability to leverage machine learning with Python to visualize and integrate AI.

Artificial Intelligence (AI) gets your system to think smart and learn intelligently. This book is packed with some of the smartest trending examples with which you will learn the fundamentals of AI. By the end, you will have acquired the basics of AI by practically applying the examples in this book.

Artificial Intelligence(AI), gets your system to think smart and intelligent. This book is packed with some of the smartest and easy-peasy examples through which you will learn the fundamentals of AI. You will have acquired the foundation of AI and understood the practical case studies in this book.