Deep Learning

Course content(Tentative) :

• Introduction to deep learning (DL)
• Deep neural networks (DNN)
• Convolutional neural network (CNN)
• Recurrent neural network (RNN)
• Transformers, Vision transformers (VIT)
• Generative models,
• Explainable AI

On successful completion of the module, students will be able to:

• Possess advanced knowledge within the area of deep learning
• Understand the meaning of concepts such as multi-layer perceptron, convolutional networks, and transformers.
• Possess specialized insight and a good understanding of the research frontier of deep learning techniques and algorithms for wide range of applications, including but not limited to visual computing.

Skills and general competence:

• Be able to use relevant and suitable methods when carrying out further research and development activities in the area of deep learning for visual computing and natural language processing.
• Be able to critically review relevant literature when solving an assigned problem or topic.
• Is able to communicate academic issues, analysis, and conclusions, with specialists in the field, in oral and written forms.
• Is experienced in acquiring new knowledge and skills in a self-directed manner.
• Develop a course project based on an application scenario and implement several of the algorithms to solve practical problems.
• The students will also enhance their programming skills in Pytorch and Tensorflow

Lectures, exercises, self-study, presentation and obligatory course project. This course will focus on practical implementation of deep learning for visual computing.