ECE6179 Neural networks and deep learning

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 optimization and generalization 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.