Lecture 1
Introduction to Deep Learning and Neural Networks
Lecture 2
Learning with Neural Networks
Lecture 3
Deeper into Deep Learning and Optimizations
Lecture 4
Convolutional Neural Networks for Computer Vision
Lecture 5
Understanding Convnets Visually and Intuitively
Lecture 6
Convnets for Object detection, Segmentation
Lecture 7
Group Equivariant Convnets (invited talk by T. Cohen)
Lecture 8
Language Representations (invited talk by C. Monz)
Lecture 9
Recurrent Neural Networks
Lecture 10
Memory Networks and Recursive Networks
Lecture 11
Bleeding edge deep learning (student presentations #1)
Lecture 12
Bleeding edge deep learning (student presentations #2)
Lecture 13
Restricted Boltzmann Machines, Autoencoders
Lecture 14
Bayesian Inference, Graphical Models and Neural Networks