Description
The aim of the course is to cover deep learning algorithms, architectures and systems, and to present applications in various modern data processing and analysis tasks.The course content is divided in the following parts.
Part I presents concepts around deep neural networks for supervised learning (regression and classification), including forward- and back-propagation, various modern gradient descent based optimization algorithms (e.g., RMSProp, Adam), loss functions and regularization methods. This part covers various deep neural network architectures including fully-connected neural networks, convolutional neural networks, recurrent neural networks, attention mechanisms and graph convolutional neural networks. Applications of such models in computer vision, natural language processing and data mining are also discussed.
Part II presents deep learning models for unsupervised learning, including autoencoders and their regularized and denoising versions. This part also covers deep generative models such as variational autoencoders (VAEs) and generative adversarial networks (GANs).
Part III discusses advanced topics in deep learning including explainability, learning from multimodal data, and learning from few examples.
Additional Description
6 ECTS credits24 contact hours Lecture
30 contact hours Seminar, Exercises or Practicals
40 contact hours Independent or External Form of Study
| Period | 24 Sept 2024 → 7 Jan 2025 |
|---|---|
| Event type | Course |
| Organiser | Vrije Universiteit Brussel |
| Location | Brussels, BelgiumShow on map |