Upper-level undergraduates, graduate students, and practitioners who want a rigorous, math-focused foundation. Not ideal for: Absolute beginners or those seeking hands-on code examples.
: Added background material on linear algebra and optimization to help students with the mathematical prerequisites. Go to product viewer dialog for this item. Introduction to Machine Learning
Anyone needing a solid, mathematically sound reference text for algorithm derivation.
Essential for understanding sequence-based data like speech and text.
: This edition introduces a dedicated chapter on deep learning, covering the training, regularizing, and structuring of deep neural networks like Convolutional Neural Networks (CNNs) Generative Adversarial Networks (GANs) Reinforcement Learning