Fundamentals of deep learning

What it covers

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), 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 stochastic 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.

Assessment

Hurdle tasks must be passed regardless of your overall mark.

When and where