This module looks into the training of modern deep neural networks: backpropagation, regularisation, automatic differentiation, computational graphs. Introduces different types of deep neural networks, such as, LSTM, convolutional networks, and autoencoders. Presents the theoretical underpinnings of deep learning and its mechanisms. Delves into selected recent advanced topics in deep learning. Examines applications of deep learning.
Private Study Hours: 124
Contact Hours: 26
Total Hours: 150
Method of assessment
This module will be assessed by 100% coursework.
25% Practical assignment 1 (individual/group; approximately 20 hours)
25% Time limited assessment 1 (individual; 1 hour with 10 hours revision)
25% Practical assignment 2 (individual/group; approximately 20 hours)
25% Time limited assessment 2 (individual; 1 hour with 10 hours revision)
Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. Deep learning. MIT press, 2017.
Kelleher, John D. Deep learning. MIT press, 2019.
Sejnowski, Terrence J. The deep learning revolution. MIT press, 2018.
1. Comprehend the benefits of data re-representation in deep neural networks, and their ensuing modelling capacity.
2. Demonstrate an understanding of the algorithms that are required to train deep neural networks.
3. Demonstrate an awareness of computational and practical challenges existing in deep learning.
4. Demonstrate a systematic understanding of the key parameters in a neural network's architecture.
5. Competently use deep learning software to solve practical problems.
6. Understand the objectives of explainability and interpretability of neural networks.
7. Explain the differences between the major deep learning architectures.
8. Evaluate the strengths and weaknesses of the state of the art deep learning models and algorithms.
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Credit level 7. Undergraduate or postgraduate masters level module.
- ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
- The named convenor is the convenor for the current academic session.
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