03927nam 22005293 450 991101185820332120241128080256.097818362006801836200684(MiAaPQ)EBC31804715(Au-PeEL)EBL31804715(CKB)36676816200041(FR-PaCSA)88961481(FRCYB88961481)88961481(DE-B1597)730425(DE-B1597)9781836200680(OCoLC)1474244219(EXLCZ)993667681620004120241128d2023 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierBefore Machine Learning Volume 2 - Calculus for A. I The Fundamental Mathematics for Data Science and Artificial Intelligence1st ed.Birmingham :Packt Publishing, Limited,2023.©2023.1 online resource (314 pages)9781836200697 1836200692 Before Machine Learning Volume 2 - Calculus for A.I: The Fundamental Mathematics for Data Science and Artificial IntelligenceDeepen your calculus foundation for AI and machine learning with essential concepts like derivatives, integrals, and multivariable calculus, all applied directly to neural networks and optimization.Key FeaturesA step-by-step guide to calculus concepts tailored for AI and machine learning applicationsClear explanations of advanced topics like Taylor Series, gradient descent, and backpropagationPractical insights connecting calculus principles directly to neural networks and data scienceBook DescriptionThis book takes readers on a structured journey through calculus fundamentals essential for AI. Starting with “Why Calculus?” it introduces key concepts like functions, limits, and derivatives, providing a solid foundation for understanding machine learning. As readers progress, they will encounter practical applications such as Taylor Series for curve fitting, gradient descent for optimization, and L'Hôpital’s Rule for managing undefined expressions. Each chapter builds up from core calculus to multidimensional topics, making complex ideas accessible and applicable to AI. The final chapters guide readers through multivariable calculus, including advanced concepts like the gradient, Hessian, and backpropagation, crucial for neural networks. From optimizing models to understanding cost functions, this book equips readers with the calculus skills needed to confidently tackle AI challenges, offering insights that make complex calculus both manageable and deeply relevant to machine learning.What you will learnExplore the essentials of calculus for machine learningCalculate derivatives and apply them in optimization tasksAnalyze functions, limits, and continuity in data scienceApply Taylor Series for predictive curve modelingUse gradient descent for effective cost-minimizationImplement multivariable calculus in neural networksWho this book is for Aspiring AI engineers, machine learning students, and data scientists will find this book valuable for building a strong calculus foundation. A basic understanding of calculus is beneficial, but the book introduces essential concepts gradually for all levels.COMPUTERS / Intelligence (AI) & SemanticsbisacshMATHEMATICS / CalculusbisacshMATHEMATICS / Differential Equations / GeneralbisacshCOMPUTERS / Intelligence (AI) & SemanticsMATHEMATICS / CalculusMATHEMATICS / Differential Equations / GeneralBrasil Jorge1831314MiAaPQMiAaPQMiAaPQBOOK9911011858203321Before Machine Learning Volume 2 - Calculus for A. I4403499UNINA