{"product_id":"transformers-in-action","title":"Transformers in Action","description":"\u003cp\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cb\u003eUnderstand the architecture that underpins today’s most powerful AI models.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eTransformers are the superpower behind large language models (LLMs) like ChatGPT, Gemini, and Claude. \u003ci\u003eTransformers in Action\u003c\/i\u003e gives you the insights, practical techniques, and extensive code samples you need to adapt pretrained transformer models to new and exciting tasks.\u003c\/p\u003e\u003cp\u003eInside \u003ci\u003eTransformers in Action\u003c\/i\u003e you’ll learn:\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eHow transformers and LLMs work\u003c\/li\u003e\n\u003cli\u003eModeling families and architecture variants\u003c\/li\u003e\n\u003cli\u003eEfficient and specialized large language models\u003c\/li\u003e\n\u003cli\u003eAdapt HuggingFace models to new tasks\u003c\/li\u003e\n\u003cli\u003eAutomate hyperparameter search with Ray Tune and Optuna\u003c\/li\u003e\n\u003cli\u003eOptimize LLM model performance\u003c\/li\u003e\n\u003cli\u003eAdvanced prompting and zero\/few-shot learning\u003c\/li\u003e\n\u003cli\u003eText generation with reinforcement learning\u003c\/li\u003e\n\u003cli\u003eResponsible LLMs\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003ci\u003eTransformers in Action\u003c\/i\u003e takes you from the origins of transformers all the way to fine-tuning an LLM for your own projects. Author \u003cb\u003eNicole Koenigstein\u003c\/b\u003e demonstrates the vital mathematical and theoretical background of the transformer architecture practically through executable Jupyter notebooks. You’ll discover advice on prompt engineering, as well as proven-and-tested methods for optimizing and tuning large language models. Plus, you’ll find unique coverage of AI ethics, specialized smaller models, and the decoder encoder architecture.\u003c\/p\u003e\u003ch5\u003eAbout the technology\u003c\/h5\u003e\u003cp\u003eTransformers are the beating heart of large language models (LLMs) and other generative AI tools. These powerful neural networks use a mechanism called self-attention, which enables them to dynamically evaluate the relevance of each input element in context. Transformer-based models can understand and generate natural language, translate between languages, summarize text, and even write code—all with impressive fluency and coherence.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eAbout the book\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003ci\u003eTransformers in Action\u003c\/i\u003e introduces you to transformers and large language models with careful attention to their design and mathematical underpinnings. You’ll learn why architecture matters for speed, scale, and retrieval as you explore applications including RAG and multi-modal models. Along the way, you’ll discover how to optimize training and performance using advanced sampling and decoding techniques, use reinforcement learning to align models with human preferences, and more. The hands-on Jupyter notebooks and real-world examples ensure you’ll see transformers in action as you go.\u003c\/p\u003e\u003ch4\u003ewhat's inside\u003c\/h4\u003e\u003cul\u003e\n\u003cli\u003eOptimizing LLM model performance\u003c\/li\u003e\n\u003cli\u003eAdapting HuggingFace models to new tasks\u003c\/li\u003e\n\u003cli\u003eHow transformers and LLMs work under the hood\u003c\/li\u003e\n\u003cli\u003eMitigating bias and responsible ethics in LLMs\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003e\u003cbr\u003e\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e\u003cb\u003eEbook License\u003c\/b\u003e\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003e\u003c\/strong\u003e\u003cspan style=\"font-weight: 700;\"\u003eEnd-User Warranty And License Agreement\u003c\/span\u003e\u003c\/p\u003e\u003cp\u003e1. Grant Of License\u003c\/p\u003e\u003cp\u003eManning Has Authorized The Download By You Of An Unrestricted Number Of Copies Of The Electronic Book (Ebook) In Any Of The Available Formats. Manning Grants You A Nonexclusive, Nontransferable License To Use The Ebook According To The Terms And Conditions Herein. This License Agreement Permits You To Install The Ebook On Any And All Your Devices For Your Personal Use Only.\u003c\/p\u003e\u003cp\u003e2. Restrictions\u003c\/p\u003e\u003cp\u003eYou Shall Not: (1) Share, Resell, Rent, Assign, Timeshare, Distribute, Or Transfer All Or Part Of The Ebook Or Any Rights Granted Hereunder To Any Other Person; (2) Duplicate The Ebook, Except For A Single Backup Or Archival Copy; (3) Remove Any Proprietary Notices, Labels, Or Marks From The Ebook; (4) Transfer Or Sublicense Title To The Ebook To Any Other Party.\u003c\/p\u003e\u003cp\u003e3. Intellectual Property Protection\u003c\/p\u003e\u003cp\u003eThe Ebook Is Owned By Manning And Is Protected By United States And International Copyright And Other Intellectual Property Laws. Manning Reserves All Rights In The Ebook Not Expressly Granted Herein. This License And Your Right To Use The Ebook Terminate Automatically If You Violate Any Part Of This Agreement. In The Event Of Termination, You Must Remove The Original And Any Copies Of The Ebook From All Your Devices.\u003c\/p\u003e\u003cp\u003e4. Source Code Supplementary Material\u003c\/p\u003e\u003cp\u003eAny Source Code Files Provided As A Supplement To The Book Are Freely Available To The Public For Download. Reuse Of The Code Is Permitted, In Whole Or In Part, Including The Creation Of Derivative Works, Provided That You Acknowledge That You Are Using It And Identify The Source: Title, Publisher And Year.\u003c\/p\u003e\u003cp\u003e5. Limited Warranty\u003c\/p\u003e\u003cp\u003eManning Warrants That The Ebook Files, A Copy Of Which You Are Authorized To Download, Are Free From Defects In The Operational Sense That They Can Be Read By A Pdf Reader Or Epub Reader, Or Other. Except For This Express Limited Warranty, Manning Makes And You Receive No Warranties, Express, Implied, Statutory Or In Any Communication With You, And Manning Specifically Disclaims Any Other Warranty Including The Implied Warranty Of Merchantability Or Fitness Or A Particular Purpose. Manning Does Not Warrant That The Operation Of The Ebook Will Be Uninterrupted Or Error Free. If The Ebook Was Purchased In The United States, The Above Exclusions May Not Apply To You As Some States Do Not Allow The Exclusion Of Implied Warranties. In Addition To The Above Warranty Rights, You May Also Have Other Rights That Vary From State To State.\u003c\/p\u003e\u003cp\u003e6. Limitation Of Liability\u003c\/p\u003e\u003cp\u003eIn No Event Will Manning Be Liable For Any Damages, Whether Arising For Tort Or Contract, Including Loss Of Data, Lost Profits, Or Other Special, Incidental, Consequential, Or Indirect Damages Arising Out Of The Use Or Inability To Use The Ebook.\u003c\/p\u003e\u003cp\u003e7. General\u003c\/p\u003e\u003cp\u003eThis Agreement Constitutes The Entire Agreement Between You And Manning And Supersedes Any Prior Agreement Concerning The Ebook. This Agreement Is Governed By The Laws Of The State Of New York.\u003c\/p\u003e\u003ch3\u003eAbout the Author\u003c\/h3\u003e\u003cp\u003e\u003cb\u003eNicole Koenigstein\u003c\/b\u003e is a distinguished Data Scientist and Quantitative Researcher. She is presently the Chief Data Scientist and Head of AI \u0026amp; Quantitative Research at Wyden Capital.\u003c\/p\u003e\u003cp\u003e\u003cbr\u003e\u003c\/p\u003e","brand":"Manning Publications","offers":[{"title":"Paperback","offer_id":50506631413830,"sku":"9781633437883","price":5318.0,"currency_code":"INR","in_stock":true},{"title":"E-Book","offer_id":50506631446598,"sku":"9781633437883-2","price":3782.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0754\/2797\/1142\/files\/61OIlYhxT3L._SY342.jpg?v=1790331396","url":"https:\/\/cb-india.com\/products\/transformers-in-action","provider":"Computer Bookshop (India) Pvt Ltd","version":"1.0","type":"link"}