Upon successful completion of this course, the students should:
- Understand the underlying concepts and properties related to learning by Deep Neural Networks (DNNs), including its formulation as empirical risk minimization problems and the different training methods.
- Be able to evaluate the performance of common DNN architectures/types, such as deep convolutional/recurrent neural networks, as well as factor models, analyzing also the practical issues.
- Know how to formulate and apply the Deep Learning framework to solve several practical problems in different application domains.
This course offers an in-depth study on the mathematical and algorithmic foundations of deep neural networks (DNNs).
The course covers the following main topics:
Faculty of Engineering and Science