Covers the back-propagation algorithm for deep neural networks and the importance of locality in CNN.
Introduces fundamental machine learning concepts, covering regression, classification, dimensionality reduction, and deep generative models.
Covers Multilayer Perceptrons, artificial neurons, activation functions, matrix notation, flexibility, regularization, regression, and classification tasks.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
