Abstract:
In this tutorial, the participants will have hands-on experience on the newly released Tensorflow 2.0 library for deep learning applications. Deep learning has had a tangible impact on a number of fields in the recent years. Many libraries help people build deep architectures. Tensorflow is one of the most popular open source libraries out there. In this tutorial, we will go over the basics of Tensorflow 2.0. Then we will learn how to build a neural network, a deep convolutional neural network and long-short term memory networks in Tensorflow 2.0 with practical examples. If we have enough time left, in addition, we may develop a Generative Adversarial Networks as well. We will cover the structures of the different networks in brief; however, this will not be the focus of the tutorial. The focus will primarily be on the practical implementation. The participants are expected to have a working knowledge of Python and NumPy. In addition, the participants are expected to have working knowledge of machine learning and different architectures developed in the tutorial.
Professor: Usman Tariq
American University of Sharjah, UAE
Biography:
Dr. Usman Tariq is a faculty member in the Electrical Engineering Department at the American University of Sharjah, Sharjah, UAE. He has over 10 years of experience in machine learning, image processing and computer vision. Before AUS, he worked as a Research Scientist in the Computer Vision group at the Xerox Research Center Europe, France. He earned his M.S. and Ph.D. degrees from the Electrical and Computer Engineering Department of University of Illinois at Urbana-Champaign (UIUC), respectively in 2009 and 2013. His research interests include computer vision, image processing, and machine learning.