Teaching Responsibility

LJMU Schools involved in Delivery:

Computer Science and Mathematics

Learning Methods

Lecture
Practical
Tutorial

Module Offerings

7144COMP-SEP-CTY

Aims

To develop knowledge of effective and academic understanding of deep learning at masters level and provide guidance on the purpose, design and development of deep learning projects. To provide an understanding of how the range of tools, techniques and algorithms can be most appropriately applied. To provide help on establishing best practice deep learning design and development principles to successfully complete a deep learning project.

Learning Outcomes

1.
Demonstrate a critical understanding of the theoretical principles and objectives of Deep Learning (DL)
2.
Critically assess and select a range of DL concepts and techniques.
3.
Critically select appropriate DL algorithms and architectures to solve particular tasks
4.
Implement and test different DL algorithms and architectures using Python and associated frameworks
5.
Evaluate DL algorithms and architectures to determine their strengths and weaknesses

Module Content

Outline Syllabus:1. Introduction to Deep Learning 2. Multi-Layer Perceptions 3. Gradient Descent and Backpropagation 4. Hyper-parameter optimisation 5. Supervised Learning (Artificial Neural Networks) 6. Unsupervised Learning (Stacked Autoencoders) 7. Convolutional Neural Networks 8. Training, Evaluation and Regularisation 9. Optimisation 10. Deployment and Hosting 11. Applications in Deep Learning; Limitations and New Frontiers
Module Overview:
This module provides fundamental skills required in deep learning to conduct a wide variety of projects from signal processing to object detection and segmentation.
Additional Information:This module provides fundamental skills required in deep learning to conduct a wide variety of projects from signal processing to object detection and segmentation. These skills will help to equip the student with the key principles of deep learning to support advanced topics taught in the course. Furthermore, these skills will be practical core requirements for a successful career as a deep learning practitioner in industry.

Assessments

Technology
Report