Teaching GuideTerm Faculty of Computer Science |
Máster Universitario en Intelixencia Artificial |
Subjects |
Deep Learning |
Contents |
|
|
|
Identifying Data | 2022/23 | |||||||||||||
Subject | Deep Learning | Code | 614544013 | |||||||||||
Study programme |
|
|||||||||||||
Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
Official Master's Degree | 2nd four-month period |
First | Optional | 6 | ||||||||||
|
Topic | Sub-topic |
1. Introduction to deep learning | Shallow learning Deep learning |
2. Regularization and optimization in deep learning | Regularization via data Regularization via model Regularization via objective function Optimization |
3. Convolutional neural networks (CNNs) | Convolutions Pooling CNN architectures |
4. Recurrent neural networks (RNNs) | Simple recurrent networks LSTM networks GRU networks |
5. Autoencoders | How autoencoding works Anomaly detection autoencoders Denoising autoencoders |
6. Generative Adversial Networks (GANs) | Generative modeling with variational autoencoders GAN networks Deep convolutional GANs |
7. Transfer learning | How transfer learning works Transfer learning approaches |
8. Other deep learning techniques | Multi-task learning Transformers |
|