: A completely new chapter dedicated to deep learning, covering training, regularizing, and structuring architectures like Convolutional Neural Networks (CNNs) Generative Adversarial Networks (GANs) Advanced Neural Networks : New material on autoencoders network, and the popular dimensionality reduction method Reinforcement Learning
Suitable for advanced undergraduate and graduate-level courses in computer science, data science, and engineering. Key Features and Updates in the 4th Edition
Probabilistic approaches to classification. : A completely new chapter dedicated to deep
Learn which algorithm (e.g., Support Vector Machines vs. Random Forests) is best for specific data types and problems.
: Explains equations in a way that helps students translate them into computer programs. Cons : Random Forests) is best for specific data types and problems
Zero Python, R, or MATLAB. Exercises are theoretical proofs or derivations. No companion notebook. You’ll need a separate resource (e.g., Géron, Müller, or online courses) for practical skills.
New material on deep reinforcement learning, policy gradient methods, and the use of deep networks within the RL framework. Exercises are theoretical proofs or derivations
This edition features substantial updates to reflect the rapid evolution of the field since the previous release:
The most reliable way to access the book is through university libraries or platforms like O'Reilly Online Learning and Google Books , which often offer digital rentals.