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Teaching GuideTerm Faculty of Computer Science |
| Máster Universitario en Intelixencia Artificial |
 Subjects |
| Deep Learning |
| Contents |
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| Identifying Data | 2022/23 | |||||||||||||
| Subject | Deep Learning | Code | 614544013 | |||||||||||
| Study programme |
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| Descriptors | Cycle | Period | Year | Type | Credits | |||||||||
| Official Master's Degree | 2nd four-month period |
First | Optional | 6 | ||||||||||
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| 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 |
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