Neural Networks and Deep Learning This course aims to introduce students to the main topics and methods in the field of neural networks and deep learning, ranging from traditional neural network models to the latest research and applications of deep learning.
Topics chosen from: perceptrons, feedforward neural networks, backpropagation, deep convolutional networks for image processing; geometric analysis of trained neural networks; recurrent networks, language processing, semantic analysis, long short term memory; Hopfield networks, restricted Boltzmann machines and autoencoders, generative adversarial networks; deep reinforcement learning; designing successful applications of neural networks; recent developments in neural networks and deep learning.