Deep learning
| Class semester | 2nd semester |
|---|---|
| Class type | Mandatory |
| Instructors | 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
