Category:

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 3, 20199:30 am12:00 pm Pablo Piantanida
July 3, 20191:30 pm4:30 pm Pablo Piantanida
July 3, 2019
9:30 am - 12:00 pm - with Pablo Piantanida - at
July 3, 2019
1:30 pm - 4:30 pm - with Pablo Piantanida - at

Class Information

 Open: July 3, 2019

Class Trainer