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Whether you are new to AI or looking to enhance your skills, this book provides comprehensive guidance on implementing LLMs in real-world scenarios.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eTable of Contents\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003e- Undersstanding the LLM Twin Concept and Architecture\u003c\/p\u003e\u003cp\u003e- Tooling and Installation\u003c\/p\u003e\u003cp\u003e- Data Engineering\u003c\/p\u003e\u003cp\u003e- RAG Feature Pipeline\u003c\/p\u003e\u003cp\u003e- Supervised Fine-tuning\u003c\/p\u003e\u003cp\u003e- Fine-tuning with Preference Alignment\u003c\/p\u003e\u003cp\u003e- Evaluating LLMs\u003c\/p\u003e\u003cp\u003e- Inference Optimization\u003c\/p\u003e\u003cp\u003e- RAG Inference Pipeline\u003c\/p\u003e\u003cp\u003e- Inference Pipeline Deployment\u003c\/p\u003e\u003cp\u003e- MLOps and LLMOps\u003c\/p\u003e\u003cp\u003e- Appendix: MLOps Principles\u003c\/p\u003e\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAuthor:\u003c\/b\u003e Paul Iusztin,Maxime Labonne\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Packt Publishing\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 10\/22\/2024\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 522\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 1.96lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.25h x 7.50w x 1.05d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781836200079\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003e\u003ci\u003eLabonne, Maxime:\u003c\/i\u003e\u003c\/b\u003e - Maxime Labonne is a Senior Staff Machine Learning Scientist at Liquid AI, serving as the head of post-training. He holds a Ph.D. in Machine Learning from the Polytechnic Institute of Paris and is recognized as a Google Developer Expert in AI\/ML.An active blogger, he has made significant contributions to the open-source community, including the LLM Course on GitHub, tools such as LLM AutoEval, and several state-of-the-art models like NeuralBeagle and Phixtral. He is the author of the best-selling book \"Hands-On Graph Neural Networks Using Python,\" published by Packt.Connect with him on X and LinkedIn at maximelabonne.\u003cb\u003e\u003ci\u003eIusztin, Paul:\u003c\/i\u003e\u003c\/b\u003e - Paul Iusztin is a senior ML and MLOps engineer at Metaphysic, a leading GenAI platform, serving as one of their core engineers in taking their deep learning products to production. Along with Metaphysic, with over seven years of experience, he built GenAI, Computer Vision and MLOps solutions for CoreAI, Everseen, and Continental. 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It clarifies artificial intelligence and data science, blending core mathematical principles with a clear, reader-friendly approach. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eUnlike traditional textbooks that lean heavily on equations and mathematical formalization, the author starts with minimal prerequisites, layering deeper math as the reader progresses. Each concept, algorithm, or model is unpacked through clear, hands-on examples that build the reader's skills step by step. It strikes a balance between theoretical foundations and practical application, serving as both an academic reference and a practical guide.\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003eFurthermore, the book uses humor, casual language, and comics to make the challenging concepts and topics relatable and fun. 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In this expanded and updated third edition, Keras creator \u003cb\u003eFrançois Chollet\u003c\/b\u003e offers insights for both novice and experienced machine learning practitioners. You'll master state-of-the-art deep learning tools and techniques, from the latest features of Keras 3 to building AI models that can generate text and images. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the technology\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e In less than a decade, deep learning has changed the world--twice. First, Python-based libraries like Keras, TensorFlow, and PyTorch elevated neural networks from lab experiments to high-performance production systems deployed at scale. And now, through Large Language Models and other generative AI tools, deep learning is again transforming business and society. In this new edition, Keras creator François Chollet invites you into this amazing subject in the fluid, mentoring style of a true insider. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the book\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003ci\u003eDeep Learning with Python, Third Edition\u003c\/i\u003e makes the concepts behind deep learning and generative AI understandable and approachable. This complete rewrite of the bestselling original includes fresh chapters on transformers, building your own GPT-like LLM, and generating images with diffusion models. Each chapter introduces practical projects and code examples that build your understanding of deep learning, layer by layer. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eWhat's inside\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e - Hands-on, code-first learning\u003cbr\u003e - Comprehensive, from basics to generative AI\u003cbr\u003e - Intuitive and easy math explanations\u003cbr\u003e - Examples in Keras, PyTorch, JAX, and TensorFlow \u003cp\u003e\u003c\/p\u003e\u003cb\u003eAbout the reader\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e For readers with intermediate Python skills. No previous experience with machine learning or linear algebra required. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eAbout the author\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e \u003cb\u003eFrançois Chollet\u003c\/b\u003e is the co-founder of Ndea and the creator of Keras. \u003cb\u003eMatthew Watson\u003c\/b\u003e is a software engineer at Google working on Gemini and a core maintainer of Keras. \u003cp\u003e\u003c\/p\u003e \u003cb\u003eTable of Contents\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003e 1 What is deep learning?\u003cbr\u003e 2 The mathematical building blocks of neural networks\u003cbr\u003e 3 Introduction to TensorFlow, PyTorch, JAX, and Keras\u003cbr\u003e 4 Classification and regression\u003cbr\u003e 5 Fundamentals of machine learning\u003cbr\u003e 6 The universal workflow of machine learning\u003cbr\u003e 7 A deep dive on Keras\u003cbr\u003e 8 Image classification\u003cbr\u003e 9 ConvNet architecture patterns\u003cbr\u003e 10 Interpreting what ConvNets learn\u003cbr\u003e 11 Image segmentation\u003cbr\u003e 12 Object detection\u003cbr\u003e 13 Timeseries forecasting\u003cbr\u003e 14 Text classification\u003cbr\u003e 15 Language models and the Transformer\u003cbr\u003e 16 Text generation\u003cbr\u003e 17 Image generation\u003cbr\u003e 18 Best practices for the real world\u003cbr\u003e 19 The future of AI\u003cbr\u003e 20 Conclusions \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\u003cb\u003eAuthor:\u003c\/b\u003e Francois Chollet,Matthew Watson\u003cbr\u003e\u003cb\u003ePublisher:\u003c\/b\u003e Manning Publications\u003cbr\u003e\u003cb\u003ePublished:\u003c\/b\u003e 11\/18\/2025\u003cbr\u003e\u003cb\u003ePages:\u003c\/b\u003e 648\u003cbr\u003e\u003cb\u003eBinding Type:\u003c\/b\u003e Paperback\u003cbr\u003e\u003cb\u003eWeight:\u003c\/b\u003e 2.77lbs\u003cbr\u003e\u003cb\u003eSize:\u003c\/b\u003e 9.18h x 7.35w x 1.45d\u003cbr\u003e\u003cb\u003eISBN:\u003c\/b\u003e 9781633436589\u003cbr\u003e\u003cbr\u003e\u003cb\u003eAbout the Author\u003c\/b\u003e\u003cbr\u003e\u003cb\u003eFrançois Chollet\u003c\/b\u003e is a software engineer at Google and creator of the Keras deep learning library. \u003cp\u003e\u003c\/p\u003e\u003cb\u003eMatthew Watson\u003c\/b\u003e is a core maintainer of the Keras deep learning library, focusing primarily on tools for Natural Language Processing.\u003cbr\u003e","brand":"booksdeli.com","offers":[{"title":"Francois Chollet \/ Paperback \/ English","offer_id":47464005402781,"sku":"9781633436589","price":127.98,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0619\/5648\/9373\/files\/img_af2c6545-b330-4a70-8212-f287072d4d43.jpg?v=1764943555"},{"product_id":"deep-learning-with-pytorch-second-edition-training-and-applying-deep-learning-and-generative-ai-models-9781633438859","title":"Deep Learning with Pytorch, Second Edition: Training and Applying Deep Learning and Generative AI Models","description":"\u003cb\u003eGet 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.\u003c\/b\u003e \u003cp\u003e\u003c\/p\u003ePyTorch core developer Howard Huang updates the bestselling original Deep Learning with PyTorch with new insights into the transformers architecture and generative AI models. \u003cp\u003e\u003c\/p\u003e Instantly familiar to anyone who knows PyData tools like NumPy, PyTorch simplifies deep learning without sacrificing advanced features. 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