Numerical Methods for Machine Learning: Optimization, Stability, and Algorithms

Master the mathematics that actually powers modern machine learning systems.Numerical Methods for Machine Learning: Optimization, Stability, and Algorithms bridges the gap between theoretical machine learning and the numerical computation that makes real-world AI systems work. While most ML books focus on models and architectures, this book reveals what happens underneath the equations - where floating-point precision, conditioning, optimization dynamics, and numerical stability determine whether models converge, fail, or scale successfully.Designed for advanced students, machine learning engineers, data scientists, and quantitative developers, this practical guide explains how numerical methods shape every stage of machine learning, from gradient descent and matrix factorization to deep learning optimization and probabilistic computation.Inside this book, you will learn: Floating-point arithmetic and machine precision Conditioning, stability, and error propagation Numerical linear algebra for machine learning Matrix decompositions, eigenvalues, and singular values Gradient descent, Newton methods, and constrained optimization Numerical issues in deep neural networks Stable implementations of softmax, cross-entropy, and normalization Exploding and vanishing gradients Probabilistic computation and log-sum-exp techniques Robust ML pipelines and large-scale optimization systems Practical numerical debugging strategies used in real ML systemsUnlike purely theoretical texts, this book focuses on the numerical realities engineers face in production:

Pages: 243, Paperback, Independently published