Deep Learning

Aim

After completion of the course the student should at an advanced level be able to:

• use relevant concepts and methods from Deep Learning in order to formulate, structure and solve practical problems that involve large and complex data
• choose a deep learning architecture that is appropriate for a given data structure, problem formulation and application area
• choose appropriate activation functions and hyperparameter settings in Deep Learning models
• estimate the performance of Deep Learning models
• use existing Deep Learning software in order to analyze large and complex datasets, tune the network architecture and make predictions.

Content

The course introduces main concepts in Deep Learning and widely used Deep Learning models. The course includes the following topics:

• Deep and shallow networks
• Regularization, droupout and early stopping. Optimization of deep neural networks
• Convolutional neural networks and image analysis
• Deep recurrent neural networks and sequence analysis
• Autoencoders and feature extraction
• Generative Adversarial neural networks