Deep learning

What it covers

Deep learning sits behind many of the most visible advances in modern artificial intelligence, from computer vision and natural language processing to generative systems and decision support. This unit develops the conceptual and practical capability needed to understand how deep learning systems work, when they are effective, and where their limitations emerge. You will study neural networks and deep learning within the broader context of machine learning, including backpropagation, optimisation, convolutional networks, recurrent architectures, attention mechanisms, transformers, embeddings and adversarial robustness. Through practical activities, you will use contemporary deep learning methods and tools to construct, train, evaluate and refine models for tasks such as image recognition, object recognition and text classification. The unit emphasises judgement, interpretation and communication as well as implementation. You will analyse why deep learning models succeed or fail, select appropriate architectures and training strategies, evaluate performance and robustness, and communicate technical findings, model behaviour and limitations for appropriate audiences and decision-making contexts.

Assessment

Assessment not published yet.

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