MATHEMATICS OF ARTIFICIAL INTELLIGENCE Linear Algebra, Calculus, Probability & Optimization: The Essential Mathematical Foundation for Machine Learning, Deep Learning, and Generative AI - Softcover

Inhaltsangabe

Artificial intelligence is powered by mathematics. Behind every machine learning model, neural network, and large language model lies a foundation of linear algebra, calculus, probability, statistics, and optimization. Understanding these mathematical principles is the key to moving beyond using AI tools to truly understanding how they work.

Mathematics of Artificial Intelligence is a comprehensive, beginner-friendly guide that builds the mathematical foundation required for modern AI, machine learning, deep learning, and generative AI. Starting with essential algebra and progressing through advanced topics, this book explains every concept with clear language, step-by-step worked examples, practical visualizations, and real-world AI applications.

Rather than presenting mathematics as abstract theory, each chapter connects mathematical ideas directly to artificial intelligence, helping readers understand how algorithms learn from data, make predictions, optimize performance, and power today's intelligent systems.

Inside this book, you'll learn:

Essential algebra and mathematical foundations
Linear algebra for machine learning
Vector spaces, matrices, and matrix operations
Eigenvalues, eigenvectors, and singular value decomposition
Differential and integral calculus
Partial derivatives and gradients
Multivariable calculus for neural networks
Probability theory and statistical inference
Random variables and probability distributions
Bayesian statistics and conditional probability
Optimization techniques including gradient descent
Convex optimization fundamentals
Information theory and entropy
The mathematics behind deep learning
Neural networks and backpropagation
Transformers, attention mechanisms, and large language models
Numerical methods for AI
Python implementations using NumPy and scientific computing tools

Each chapter includes:

Clear explanations written for beginners
Multiple fully worked examples
AI application boxes connecting theory to practice
High-quality illustrations and diagrams
Python code examples
Practice exercises with complete solutions
Chapter summaries and key takeaways

Whether you're a student, software developer, data scientist, engineer, researcher, or self-learner, this book provides the mathematical knowledge needed to understand modern artificial intelligence with confidence.

If you want to move beyond treating AI as a black box and develop a deep understanding of the mathematics that powers today's most advanced models, Mathematics of Artificial Intelligence is your complete learning companion.

Die Inhaltsangabe kann sich auf eine andere Ausgabe dieses Titels beziehen.