ECE4179 Neural networks and deep learning

SEMESTER 1, 2025

This unit introduces fundamentals of deep learning and how it can solve problems in many areas, such as image classification, filter design and natural language processing. Neural networks are first described and how training can be achieved with backpropagation. Various forms of deep neural networks are developed, such as multilayer perceptrons, convolution neural networks and recurrent neural networks. The mathematics of stochastic optimisation is used to interpret and understand the behaviour and training of these networks. Programming approaches are discussed for training and deploying neural networks. Deep learning technologies and design examples are discussed in areas such as driverless cars, personal cognitive assistants and mastering of games such as GO.

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SEMESTER 2, 2025

This unit introduces the fundamentals of deep learning and its applications across various domains, including image classification, signal processing, and natural language understanding. Neural networks are first described, followed by how training can be achieved with backpropagation. Various forms of deep neural networks are developed, including Multilayer Perceptrons (MLPs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). Modern advancements such as transformers and Large Language Models (LLMs) are described, as well as their deployment and fine-tuning. The mathematics of optimisation and generalisation is used to interpret and understand the behaviour and training of these networks. Programming frameworks for training, fine-tuning, and deploying neural networks are discussed. Deep learning technologies and design examples are discussed in areas such as visual perception,  driverless cars, intelligent assistants, and generative AI.