Matrix Operations Practice Book for AI Learners : 20 Chapters of Worked Problems from Matrices and Eigenvalues to SVD, Backpropagation, and Transformers
English | May 11, 2026 | ASIN: B0GYFVDRB3 | 457 pages | PDF | 43.98 MB
Matrix Operations Practice Book for AI Learners is a structured, problem based workbook for students and practitioners who want to build a strong mathematical foundation for machine learning. Starting from the basics of reading and writing matrices, this book develops every core topic used in modern AI systems through clear explanations, worked examples, fill in the blank exercises, and unsolved practice problems.Across 20 chapters, readers work through matrices, vectors, dot products, matrix multiplication, transpose and inverse, determinants, eigenvalues, linear transformations, systems of equations, vector spaces, orthogonality, SVD, probability, gradients, convolutions, attention mechanisms, optimisation, regularization, dimensionality reduction, and full network mathematics. Every chapter includes an AI Connection section that shows directly how the concept is used in real machine learning systems.This workbook covers:Reading, writing, and operating on matrices and vectors.Eigenvalues, eigenvectors, and singular value decomposition.Gradients, backpropagation, and the chain rule.Convolutions, tensors, and attention mechanisms.Optimisation methods including SGD, momentum, and Adam.Regularization, PCA, t-SNE, and full network mathematics.This book is ideal for machine learning students, data scientists, AI engineers, and anyone who wants a clear, structured, and practical guide to the matrix mathematics behind modern neural networks.
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