{"product_id":"transformers-in-action-9781633437883","title":"Transformers in Action","description":"\u003cb\u003eUnderstand the architecture that underpins today's most powerful AI models.\u003c\/b\u003e \u003cp\u003e\u003c\/p\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. \u003cp\u003e\u003c\/p\u003eInside \u003ci\u003eTransformers in Action\u003c\/i\u003e you'll learn: \u003cp\u003e\u003c\/p\u003e - How transformers and LLMs work\u003cbr\u003e - Modeling families and architecture variants\u003cbr\u003e - Efficient and specialized large language models\u003cbr\u003e - Adapt HuggingFace models to new tasks\u003cbr\u003e - Automate hyperparameter search with Ray Tune and Optuna\u003cbr\u003e - Optimize LLM model performance\u003cbr\u003e - Advanced prompting and zero\/few-shot learning\u003cbr\u003e - Text generation with reinforcement learning\u003cbr\u003e - Responsible LLMs \u003cp\u003e\u003c\/p\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 Nicole Koenigstein 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. \u003cp\u003e\u003c\/p\u003e Foreword by Luis Serrano. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Transformers 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Transformers in Action 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. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e - Optimizing LLM model performance\u003cbr\u003e - Adapting HuggingFace models to new tasks\u003cbr\u003e - How transformers and LLMs work under the hood\u003cbr\u003e - Mitigating bias and responsible ethics in LLMs \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For data scientists and machine learning engineers. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eNicole Koenigstein\u003c\/b\u003e is the Co-Founder and Chief AI Officer at the fintech company Quantmate. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e Part 1\u003cbr\u003e 1 The need for transformers\u003cbr\u003e 2 A deeper look into transformers\u003cbr\u003e Part 2\u003cbr\u003e 3 Model families and architecture variants\u003cbr\u003e 4 Text generation strategies and prompting techniques\u003cbr\u003e 5 Preference alignment and retrieval-augmented generation\u003cbr\u003e Part 3\u003cbr\u003e 6 Multimodal models\u003cbr\u003e 7 Efficient and specialized small language models\u003cbr\u003e 8 Training and evaluating large language models\u003cbr\u003e 9 Optimizing and scaling large language models\u003cbr\u003e 10 Ethical and responsible large language models \u003cp\u003e\u003c\/p\u003e Get a free eBook (PDF or ePub) from Manning as well as access to the online liveBook format (and its AI assistant that will answer your questions in any language) when you purchase the print book.\u003cbr\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\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.\u003cbr\u003e","brand":"Manning Publications","offers":[{"title":"Default Title","offer_id":52196542513426,"sku":"9781633437883","price":55.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0831\/4771\/8930\/files\/img_ce2c2fa4-c3dc-4962-9403-88ec212dd685.jpg?v=1776153339","url":"https:\/\/surprise-castle.myshopify.com\/products\/transformers-in-action-9781633437883","provider":"Surprise Castle","version":"1.0","type":"link"}