Introduction to Deep Learning

This course will provide students with the principles of representation learning and deep learning by covering the following subjects: Neural Networks, Backpropagation and stochastic gradient optimisation, Auto-encoders, Hyper-parameters and training tricks for neural networks, regularization, Deep Belief Networks and Deep Boltzmann Machines. Students will apply these approaches during a practical lab session.

Class Timetable

 Start TimeEnd TimeProfessor
July 1, 202110:50 am1:00 pm Pablo Piantanida
July 1, 20212:00 pm4:10 pm Stergios Christodoulidis
July 1, 2021
10:50 am - 1:00 pm - with Pablo Piantanida - at
July 1, 2021
2:00 pm - 4:10 pm - with Stergios Christodoulidis - at

Class Information

 Open: July 1, 2021

Class Trainer