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Explores perception in deep learning for autonomous vehicles, covering image classification, optimization methods, and the role of representation in machine learning.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Introduces convolutional neural networks for image processing, covering basic components, architectures, and practical applications, including denoising and segmentation.
Introduces a functional framework for deep neural networks with adaptive piecewise-linear splines, focusing on biomedical image reconstruction and the challenges of deep splines.