{"product_id":"transformers-for-natural-language-processing-and-computer-vision-third-edition-explore-generative-ai-and-large-language-models-with-hugging-face-c-paperback","title":"Transformers for Natural Language Processing and Computer Vision - Third Edition: Explore Generative AI and Large Language Models with Hugging Face, C - Paperback","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eDenis Rothman\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eUnleash the full potential of transformers with this comprehensive guide covering architecture, capabilities, risks, and practical implementations on OpenAI, Google Vertex AI, and Hugging Face\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003ePurchase of the print or Kindle book includes a free eBook in PDF format\u003c\/strong\u003e\u003c\/p\u003eKey Features\u003cul\u003e\n\u003cli\u003eMaster NLP and vision transformers, from the architecture to fine-tuning and implementation\u003c\/li\u003e\n\u003cli\u003eLearn how to apply Retrieval Augmented Generation (RAG) with LLMs using customized texts and embeddings\u003c\/li\u003e\n\u003cli\u003eMitigate LLM risks, such as hallucinations, using moderation models and knowledge bases\u003c\/li\u003e\n\u003c\/ul\u003eBook Description\u003cp\u003eTransformers 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).\u003c\/p\u003e\u003cp\u003eThe 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.\u003c\/p\u003e\u003cp\u003eDive 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.\u003c\/p\u003e\u003cp\u003eThis book provides you with an understanding of transformer architectures, pretraining, fine-tuning, LLM use cases, and best practices.\u003c\/p\u003eWhat you will learn\u003cul\u003e\n\u003cli\u003eLearn how to pretrain and fine-tune LLMs\u003c\/li\u003e\n\u003cli\u003eLearn how to work with multiple platforms, such as Hugging Face, OpenAI, and Google Vertex AI\u003c\/li\u003e\n\u003cli\u003eLearn about different tokenizers and the best practices for preprocessing language data\u003c\/li\u003e\n\u003cli\u003eImplement Retrieval Augmented Generation and rules bases to mitigate hallucinations\u003c\/li\u003e\n\u003cli\u003eVisualize transformer model activity for deeper insights using BertViz, LIME, and SHAP\u003c\/li\u003e\n\u003cli\u003eCreate and implement cross-platform chained models, such as HuggingGPT\u003c\/li\u003e\n\u003cli\u003eGo in-depth into vision transformers with CLIP, DALL-E 2, DALL-E 3, and GPT-4V\u003c\/li\u003e\n\u003c\/ul\u003eWho this book is for\u003cp\u003eThis 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.\u003c\/p\u003e\u003cp\u003eKnowledge 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.\u003c\/p\u003eTable of Contents\u003col\u003e\n\u003cli\u003eWhat are Transformers?\u003c\/li\u003e\n\u003cli\u003eGetting Started with the Architecture of the Transformer Model\u003c\/li\u003e\n\u003cli\u003eEmergent vs Downstream Tasks: The Unseen Depths of Transformers\u003c\/li\u003e\n\u003cli\u003eAdvancements in Translations with Google Trax, Google Translate, and Gemini\u003c\/li\u003e\n\u003cli\u003eDiving into Fine-Tuning through BERT\u003c\/li\u003e\n\u003cli\u003ePretraining a Transformer from Scratch through RoBERTa\u003c\/li\u003e\n\u003cli\u003eThe Generative AI Revolution with ChatGPT\u003c\/li\u003e\n\u003cli\u003eFine-Tuning OpenAI GPT Models\u003c\/li\u003e\n\u003cli\u003eShattering the Black Box with Interpretable Tools\u003c\/li\u003e\n\u003cli\u003eInvestigating the Role of Tokenizers in Shaping Transformer Models\u003c\/li\u003e\n\u003c\/ol\u003e\u003cp\u003e(N.B. Please use the Look Inside option to see further chapters)\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 728\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.45 x 9.25 x 7.5 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e February 29, 2024\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":44434737365094,"sku":"9781805128724","price":89.35,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0599\/7255\/0758\/files\/lsWVGZEVb69781805128724.webp?v=1770461453","url":"https:\/\/infinitylightwa.com\/products\/transformers-for-natural-language-processing-and-computer-vision-third-edition-explore-generative-ai-and-large-language-models-with-hugging-face-c-paperback","provider":"Infinity Light","version":"1.0","type":"link"}