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 Hour: 150
50% Practical assignment 1 (individual; approximately 40 hours)
25% Demo on practical assignment 1 (individual; 1 hour with 10 hours revision)
25% Time limited assessment 2 (individual; 1 hour with 10 hours revision)
The University is committed to ensuring that core reading materials are in accessible electronic format in line with the Kent Inclusive Practices.
The most up to date reading list for each module can be found on the university's reading list pages.
Bengio, Yoshua, Ian Goodfellow, and Aaron Courville. Deep learning. MIT press, 2017.
Kelleher, John D. Deep learning. MIT press, 2019.
https://www.deeplearningbook.org/
Sejnowski, Terrence J. The deep learning revolution. MIT press, 2018.
The intended subject specific learning outcomes.
On successfully completing the module students will be able to:
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.
The intended generic learning outcomes.
On successfully completing the module students will be able to:
On successfully completing the level 6 module students will be able to
1 Demonstrate critical thinking and problem-solving skills.
2 Communicate with other professionals using appropriate technical vocabulary.
3 Construct reasoned arguments about pros and cons of algorithms and their implementations.
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