LEADER 03927nam 22005293 450 001 9911011858203321 005 20241128080256.0 010 $a9781836200680 010 $a1836200684 035 $a(MiAaPQ)EBC31804715 035 $a(Au-PeEL)EBL31804715 035 $a(CKB)36676816200041 035 $a(FR-PaCSA)88961481 035 $a(FRCYB88961481)88961481 035 $a(DE-B1597)730425 035 $a(DE-B1597)9781836200680 035 $a(OCoLC)1474244219 035 $a(EXLCZ)9936676816200041 100 $a20241128d2023 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aBefore Machine Learning Volume 2 - Calculus for A. I $eThe Fundamental Mathematics for Data Science and Artificial Intelligence 205 $a1st ed. 210 1$aBirmingham :$cPackt Publishing, Limited,$d2023. 210 4$d©2023. 215 $a1 online resource (314 pages) 311 08$a9781836200697 311 08$a1836200692 327 $tBefore Machine Learning Volume 2 - Calculus for A.I: The Fundamental Mathematics for Data Science and Artificial Intelligence 330 $aDeepen 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. 606 $aCOMPUTERS / Intelligence (AI) & Semantics$2bisacsh 606 $aMATHEMATICS / Calculus$2bisacsh 606 $aMATHEMATICS / Differential Equations / General$2bisacsh 615 7$aCOMPUTERS / Intelligence (AI) & Semantics 615 7$aMATHEMATICS / Calculus 615 7$aMATHEMATICS / Differential Equations / General 700 $aBrasil$b Jorge$01831314 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9911011858203321 996 $aBefore Machine Learning Volume 2 - Calculus for A. I$94403499 997 $aUNINA