Deep Learning Technologies

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• When it is and is not appropriate to use deep learning in practical problems
• The key factors that have made deep learning successful for various applications

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Characterise what types of model architecture might suit certain types of data
• Understand appropriate measures of loss and performance for different types of data

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Gain facility in fine tuning and debugging models at training time
• Apply existing deep learning models to real datasets

• Understanding problems and when to use deep learning
• MLPs, CNNs, RNNs, Transformers
• Training network and gradient descent
• Common Network Architectures
• ResNets, etc
• Data augmentation
• Transfer Learning
• Fine Tuning
• Embeddings
• Sequence Processing
• Classical methods
• Deep learning approaches
• Advanced topics
• multimodal models, recent advances, etc.
• Use cases such as:
• Detecting hate speech in social media (transformers + NLP)
• Designing molecules for use as drugs (graph neural nets)
• Diagnosing diseases from chest X-rays (transfer learning)

Teaching and learning methods

Lectures, labs and guided self-study

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Repeat Information

Repeat type: Internal & External