New material discusses the intersection of deep networks and reinforcement learning, covering advanced topics like policy gradient methods. Dimensionality and Feature Learning:
: An entirely new chapter dedicated to deep neural networks, covering training, regularization, convolutional neural networks (CNNs), and generative adversarial networks (GANs). New material discusses the intersection of deep networks
The field of Machine Learning evolves rapidly. The 4th edition addresses the "Deep Learning Revolution" and shifts in the industry that occurred between 2014 (3rd edition) and 2020. Key updates include: The 4th edition addresses the "Deep Learning Revolution"
, published by The MIT Press in 2020, is a comprehensive textbook designed for advanced undergraduates, graduate students, and industry professionals. It serves as a "Swiss Army knife" for the field, balancing theoretical foundations with practical application. to help students with the necessary mathematical background
to help students with the necessary mathematical background. Updated Techniques : Discusses for dimensionality reduction and includes new material on autoencoders Amazon.com Core Topics Covered
Bayesian Decision Theory, Parametric/Nonparametric Methods, Multivariate Analysis Unsupervised Learning Clustering, Dimensionality Reduction Specialized Models