{"product_id":"nformation-theory-for-data-science-from-entropy-to-machine-learning-ai-and-modern-analytics-9798199987813","title":"Nformation Theory for Data Science: From Entropy to Machine Learning, AI, and Modern Analytics","description":"\u003cp\u003e • Author(s): Mir Hossain\u003cbr\u003e • Publisher: Independently Published\u003cbr\u003e • Publisher Imprint: Independently Published\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - General\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eMaster the mathematics that powers modern machine learning, artificial intelligence, data analytics, and large language models.\u003c\/b\u003e\u003c\/p\u003e\u003cp\u003eInformation theory is the hidden language of data science. Every time a model minimizes cross-entropy loss, every time features are selected using mutual information, and every time an AI system predicts the next token, information theory is at work.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eInformation Theory for Data Science\u003c\/b\u003e provides a practical, modern introduction to the concepts that drive today's data-driven technologies. Starting with the foundations of probability and information, this book builds step-by-step toward entropy, divergence measures, feature selection, machine learning applications, deep learning, generative AI, and large language models.\u003c\/p\u003e\u003cp\u003eUnlike traditional information theory texts that focus primarily on communication systems, this book emphasizes real-world applications in data science and artificial intelligence, helping readers connect mathematical concepts directly to modern analytics and machine learning workflows.\u003c\/p\u003eInside You'll Learn: \u003cp\u003eSelf-information and surprisal\u003c\/p\u003e\u003cp\u003eShannon entropy and uncertainty measurement\u003c\/p\u003e\u003cp\u003eJoint, conditional, and differential entropy\u003c\/p\u003e\u003cp\u003eKL divergence and Jensen-Shannon divergence\u003c\/p\u003e\u003cp\u003eMutual information and dependency analysis\u003c\/p\u003e\u003cp\u003eFeature selection using information-theoretic methods\u003c\/p\u003e\u003cp\u003eDecision trees and entropy-based learning\u003c\/p\u003e\u003cp\u003eCross-entropy loss in machine learning\u003c\/p\u003e\u003cp\u003eInformation bottleneck theory\u003c\/p\u003e\u003cp\u003eRepresentation learning and latent information\u003c\/p\u003e\u003cp\u003eInformation theory in deep learning\u003c\/p\u003e\u003cp\u003eNatural language processing and language modeling\u003c\/p\u003e\u003cp\u003eComputer vision and image information analysis\u003c\/p\u003e\u003cp\u003eGenerative AI and probabilistic modeling\u003c\/p\u003e\u003cp\u003eData compression and source coding\u003c\/p\u003e\u003cp\u003eChannel capacity and reliable communication\u003c\/p\u003e\u003cp\u003eR�nyi entropy, Tsallis entropy, and information geometry\u003c\/p\u003e\u003cp\u003eCausal information theory\u003c\/p\u003e\u003cp\u003eInformation theory for Large Language Models (LLMs)\u003c\/p\u003ePractical Features\u003cul\u003e\n\u003cli\u003eClear explanations with intuitive examples\u003c\/li\u003e\n\u003cli\u003eMathematical derivations presented step-by-step\u003c\/li\u003e\n\u003cli\u003ePython implementations throughout the book\u003c\/li\u003e\n\u003cli\u003eReal-world machine learning case studies\u003c\/li\u003e\n\u003cli\u003eVisual diagrams and illustrations\u003c\/li\u003e\n\u003cli\u003eEnd-of-chapter exercises\u003c\/li\u003e\n\u003cli\u003eFive complete data science projects\u003c\/li\u003e\n\u003cli\u003eComprehensive formula reference\u003c\/li\u003e\n\u003cli\u003eInterview questions and solutions manual\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003eWhether you are a data scientist, machine learning engineer, AI practitioner, computer science student, researcher, or quantitative analyst, this book will help you develop a deep understanding of how information flows through modern intelligent systems-and how to use that knowledge to build better models and make better decisions.\u003c\/p\u003e\u003cp\u003e\u003cb\u003eFrom entropy to machine learning, AI, and modern analytics, discover the mathematical foundation behind the information age.\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e \u003cp\u003e\u003c\/p\u003e","brand":"Independently Published","offers":[{"title":"Paperback","offer_id":47968350470295,"sku":"9798199987813","price":3765.0,"currency_code":"INR","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9798199987813.webp?v=1782917647","url":"https:\/\/atlanticbooks.com\/products\/nformation-theory-for-data-science-from-entropy-to-machine-learning-ai-and-modern-analytics-9798199987813","provider":"Atlantic Books","version":"1.0","type":"link"}