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Explains the learning process in multi-layer neural networks, including back-propagation, activation functions, weights update, and error backpropagation.
Covers the BackProp algorithm, including initialization, signal propagation, error computation, weight updating, and complexity comparison with numerical differentiation.
Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
