Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Covers the fundamental principles and methods of machine learning, including supervised and unsupervised learning techniques.
Explores the benefits of deeper networks in deep learning and the importance of over-parameterization and generalization.
Covers key concepts in reinforcement learning, neural networks, clustering, and unsupervised learning, emphasizing their applications and challenges.
