Essential Mathematics for Artificial Intelligence and Machine Learning: From Linear Algebra to Optimisation - Math That Powers ML Models - Tapa blanda

Sinopsis

A Clear, Concise, and Practical Refresher on the Core Math Concepts Behind Modern AI and ML

Are 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.

• Linear Algebra – vectors, matrices, eigenvalues, and why they matter in ML

• Calculus – gradients, derivatives, and optimization in neural networks

• Probability & Statistics – understanding uncertainty, Bayes’ theorem, and distributions

• Discrete Math & Logic – sets, functions, and foundational reasoning

• Optimization Techniques – cost functions, gradient descent, and convexity

• Bonus Topics – Information theory, multivariable calculus, and more

• Concise explanations with visual aids and examples

• No-nonsense structure—ideal for quick review or deep study

• Suitable for self-learners, students, or professionals switching into AI/ML

• Covers just the math you need—no fluff, no filler

Whether 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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