F20ID - Introduction to Deep Learning
Course leader(s):
Aims
Introduce state-of-the-art deep learning concepts.
Investigate applications of deep learning.
Syllabus
1.1 Introduction to deep learning
2.1 Deep Learning fundamentals
3.1 Artificial Neural Networks basics
4.1 Shallow neural networks
5.1 Deep neural networks
6.1 Deep learning algorithms
Learning outcomes
By the end of the course, students should be able to do the following:
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Evaluate the capabilities, limitations, and practical challenges of applying deep learning techniques in real-world scenarios.
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Identify common deep learning architectures suitable for specific industrial applications.
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Develop deep learning solutions for a real-world problem, including the selection of architecture, data preprocessing, training, and validation.
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Effectively communicate complex deep learning concepts, methodologies, and results to both technical and non-technical audiences through clear and concise reports, presentations, and visualisations.
- Critically assess the performance of deep learning models using appropriate metrics, considering factors like computational efficiency and performance.
Further details
Curriculum explorer: Click here
SCQF Level: 10
Credits: 15
