Before Machine Learning Volume 2 - Calculus for A.I : The Fundamental Mathematics for Data Science and Artificial Intelligence
Deepen 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 Features: A step-by-step guide to calculus concepts tailored for AI and machine learning applicat...
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| Autore principale: | |
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| Natura: | Livre numérique |
| Lingua: | Anglais |
| Pubblicazione: |
Birmingham :
Packt Publishing
2024.
Paris : Cyberlibris |
| Accesso online: | Accès Université d'Orléans et IFPM |
| Nota: |
Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781836200680.jpg Cyberlibris (ScholarVox) corpus Informatique |
| Autres localisations: | Voir dans le Sudoc |
| Edition sous un autre format: | • Before Machine Learning Volume 2 - Calculus for A.I, The Fundamental Mathematics for Data Science and Artificial Intelligence, Jorge Brasil, Birmingham, Packt Publishing, 2024, 1 vol. (314 p.), 978-18-3620-069-7 |
| Riassunto: | Deepen 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 Features: A step-by-step guide to calculus concepts tailored for AI and machine learning applications ; Clear explanations of advanced topics like Taylor Series, gradient descent, and backpropagation ; Practical insights connecting calculus principles directly to neural networks and data scienceBook Description. This 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 learn: Explore the essentials of calculus for machine learning ; Calculate derivatives and apply them in optimization tasks ; Analyze functions, limits, and continuity in data science ; Apply Taylor Series for predictive curve modeling ; Use gradient descent for effective cost-minimization ; Implement multivariable calculus in neural networks. Who 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 |
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| Descrizione del documento: | Couverture. https://static2.cyberlibris.com/books_upload/136pix/9781836200680.jpg Cyberlibris (ScholarVox) corpus Informatique |
| ISBN: | 9781836200680 |
| Accesso: | L'accès en ligne est réservé aux établissements ou bibliothèques ayant souscrit l'abonnement. Cyberlibris |