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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 | 2023/24 | |||||||||||||
| 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 Deep Learning libraries Examples |
| 2. Regularization and optimization in deep learning | Introduction to regularization Regularization via data Regularization via model Regularization via objective function Optimization |
| 3. Convolutional neural networks (CNNs) | Introduction to CNNs Convolutional layer Pooling layer Fully connected layer CNNs examples Pretrained models Residual networks Inception networks Xception networks |
| 4. Recurrent neural networks (RNNs) | Sequence data Using sequence data without recurrence Simple recurrent networks LSTM networks GRU networks Advanced use of RNNs |
| 5. Autoencoders | Autoencoders Variational autoencoders |
| 6. Generative Adversial Networks (GANs) | Basics How to train GANs DCGAN and WGAN How to evaluate GANs Applications Variations of GANs GAN challenges Advanced GANs |
| 7. Diffusion models | Introduction The theory behind diffusion models Two examples of diffusion models Stable Diffusion Stable Diffusion at work |
| 8. Reinforcement learning | Basics What is Reinforcement learning Solution methods |
| 9. Transformers | Introduction Transformer blocks Encoder-only and decoder-only architectures Encoder-decoder architectures Examples of transformers |
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