University of Technology, Sydney

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42028 Deep Learning and Convolutional Neural Network

6cp; 3hpw (lab/tutorial), on campus, weekly
Requisite(s): 31250 Introduction to Data Analytics OR 32130 Fundamentals of Data Analytics
Recommended studies:

basics of statistics and probability, Python programming

Field 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.

Typical availability

Autumn session, City campus

Detailed subject description.

Access conditions

Note: The requisite information presented in this subject description covers only academic requisites. Full details of all enforced rules, covering both academic and admission requisites, are available at access conditions and My Student Admin.