Free Delivery Over $100
|
|
|||
|
||||
OverviewUnleash the full potential of transformers with this comprehensive guide covering architecture, capabilities, risks, and practical implementations on OpenAI, Google Vertex AI, and Hugging Face Purchase of the print or Kindle book includes a free eBook in PDF format 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 DescriptionTransformers 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.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 forThis 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. Full Product DetailsAuthor: Denis RothmanPublisher: Packt Publishing Limited Imprint: Packt Publishing Limited Edition: 3rd Revised edition ISBN: 9781805128724ISBN 10: 1805128728 Pages: 728 Publication Date: 29 February 2024 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: In Print  This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us. Table of ContentsTable 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 IdeationReviewsAuthor InformationDenis Rothman graduated from Sorbonne University and Paris-Diderot University, designing one of the very first word2matrix patented embedding and patented AI conversational agents. He began his career authoring one of the first AI cognitive Natural Language Processing (NLP) chatbots applied as an automated language teacher for Moet et Chandon and other companies. He authored an AI resource optimizer for IBM and apparel producers. He then authored an Advanced Planning and Scheduling (APS) solution used worldwide. Tab Content 6Author Website:Countries AvailableAll regions |
||||
