Deep Learning

Contents

Course syllabus: Background for Pattern Recognition and Machine Learning; A short introduction to feed-forward neural networks and error backpropagation; Analysis of Hopfield networks, Hebbian learning, Lyapunov energy functions and basins of attractions; Boltzmann machines, restricted Boltzmann machines; deep belief networks, sigmoid belief networks, deep autoencoders; convolutional neural networks; Application of deep architectures to speech and image processing.