Deep learning

Class semester
2nd semester
Class type
Theodoros Giannakopoulos

Topics per week

1. Introduction

2. Feedforward neural nets, backprop, regularisation

3. Optimisation and practical issues

4. Convolutional networks

5. Recurrent and Recursive Networks

6. Autoencoders and representation learning

7. The Long Short-Term Memory and Other Gated RNNs

8. Using external categorical evidence for clustering

9. Sequence Models and Attention

10. Laboratory

11 Inductive Transfer

12. Data Augmentation

13. Visiting lecture: CNN Architectures for Object Detection