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Essential Mathematics for Artificial Intelligence and Machine Learning: From Linear Algebra to Optimisation - Math That Powers ML Models
Format:
Paperback
Detalles del producto
Disponibilidad:
En stock
En stock
Peso con empaque:
0.48 kg
0.48 kg
Devolución:
Sí
Sí
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- A Clear, Concise, and Practical Refresher on the Core Math Concepts Behind Modern AI and MLAre you diving into the world of artificial intelligence or machine learning, but feel the need to revisit the math that drives it all? This book is your fast-track refresher on the essential mathematical foundations that every AI and ML practitioner needs to understand.Designed for students, data scientists, engineers, and curious professionals, this guide simplifies complex topics without sacrificing depth. Whether you're preparing for a course, brushing up for interviews, or building models from scratch, this reference will strengthen your understanding of the math powering today’s intelligent systems.🔍 What You’ll Learn:Linear Algebra – vectors, matrices, eigenvalues, and why they matter in MLCalculus – gradients, derivatives, and optimization in neural networksProbability & Statistics – understanding uncertainty, Bayes’ theorem, and distributionsDiscrete Math & Logic – sets, functions, and foundational reasoningOptimization Techniques – cost functions, gradient descent, and convexityBonus Topics – Information theory, multivariable calculus, and more✅ Key Features:Concise explanations with visual aids and examplesNo-nonsense structure—ideal for quick review or deep studySuitable for self-learners, students, or professionals switching into AI/MLCovers just the math you need—no fluff, no fillerWhether you're building neural networks, tuning models, or simply want to understand the "how" behind the "wow," Essential Mathematics for Artificial Intelligence and Machine Learning equips you with the mathematical fluency to excel in the field.
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U$S 21,74
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