Advanced Deep Learning

Description

Deep learning has pushed the boundaries in Artificial Intelligence (AI) and has been outperforming the state-of-the-art in numerous applications across a wide range of domains. These include object classification in images, information retrieval along with web search, natural language processing tasks such as automatic translation, and bioinformatics. This course will give you detailed insight into deep learning, covering algorithms, theory and tools in this exciting field.

Learning Outcome

Knowledge of:
* Convolutional neural networks

* Recurrent neural networks

* Generative neural networks, such as

* Variational autoencoders

* Generative adversarial networks (GANs)

* Theory of deep learning

Topics in deep learning, exemplified by

* Fully convolutional neural networks

* Graph neural networks

* Representation learning

* Diffusion models

* Self-supervised learning

Skills to:
* Select appropriate methodology to solve deep learning problems

* Implement selected deep learning algorithms using state-of-the-art tools

* Design and train deep learning algorithms

Competences to:
* Reflect upon the capabilities and limitations of deep learning algorithms

* Recognise and describe possible applications of deep learning methodology

* Design, optimise and use advanced deep models

* Apply the learned methodology to applications in analysis of real-world data such as images, sounds and text

* Analyse deep learning algorithms

Københavns Universitet