Members of the CityU community are welcome to join us! Apart from taking this opportunity to unite with fellow alumni, students, and faculty members, participants will receive updates on the latest progress of our School from our Dean and Programme Leaders. In the data science talk, we will share our research on using various data science approaches to confronting the COVID-19 pandemic.
Deep learning has resulted in breakthroughs in dealing with big data, in speech recognition, computer vision, natural language processing, and many other domains. It is based on deep neural networks with structures designed for various purposes. A mathematical foundation is needed to help understand modelling, and the approximation or generalization capability of deep learning models with network architectures and structures. In this talk, Professor Zhou will consider deep convolutional neural networks (CNNs) that are induced by convolutions. The convolutional architecture identifies essential differences between deep CNNs and classic neural networks. Professor Zhou will describe a mathematical theory for deep CNNs associated with rectified linear unit activation. In particular, Professor Zhou will discuss approximation and learning capability of deep CNNs dealing with functions of many variables.
High dimensional and big data relate to every science discipline and every facet of life. Data science tools allow the user to extract important features to decipher information and events behind big data. In this lecture, Professor Qin introduces some fascinating tools from others and from his work that are illuminating, fancy, and informative.