42028 Deep Learning and Convolutional Neural Network6cp; 3hpw (lab/tutorial), on campus, weekly.
Requisite(s): 31250 Introduction to Data Analytics OR 32130 Fundamentals of Data Analytics OR 36106 Machine Learning Algorithms and Applications
basics of statistics and probability, Python programming
Fields of practice: Data Analytics and Artificial Intelligence
Undergraduate and Postgraduate
The subject focuses on state-of-the-art research on deep learning and convolutional neural networks (CNNs) with practical applications. Recent advances in neural network approaches have significantly increased the performance of state-of-the-art data analytics, image recognition and object detection systems. This subject presents the details of deep learning architectures with a focus on learning end-to-end models for tasks, particularly image classification and object detection. State-of-the-art software tools are discussed and used for the implementation of image classification systems. Labs focus on setting-up deep learning libraries for image classification and object detection problems, and fine-tuning trained networks. Students learn to implement, train and test their own deep CNNs from scratch on GPUs. Student also explore how to deploy the trained models and build an AI system. Students demonstrate comprehension of state-of-the-art research individually, and then work in groups to apply them to the implementation of an image classification and object detection systems.
Autumn session, City campus
Detailed subject description.