Artificial Neural Networks and Deep Learning: Loss landscape and optimization methods
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Description
This lecture covers the error function landscape, minima, saddle points, momentum, ADAM optimizer, and the No Free Lunch Theorem in the context of artificial neural networks. It also discusses the differences between shallow and deep networks, the task of hidden neurons, and gradient descent optimization methods.
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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.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
