Deep Learning: Data Representations and Neural Networks
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Covers the fundamentals of multilayer neural networks and deep learning, including back-propagation and network architectures like LeNet, AlexNet, and VGG-16.
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.
Explores the provable benefits of overparameterization in model compression, emphasizing the efficiency of deep neural networks and the importance of retraining for improved performance.
Covers a review of machine learning concepts, including supervised learning, classification vs regression, linear models, kernel functions, support vector machines, dimensionality reduction, deep generative models, and cross-validation.
Introduces kernel methods like SVM and regression, covering concepts such as margin, support vector machine, curse of dimensionality, and Gaussian process regression.