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COMP6248 Deep Learning

Module Overview

Deep learning has revolutionised numerous fields in recent years. We've witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of cheap GPGPU compute coupled with large datasets and some neat algorithms. This module will look at how deep learning works, from theoretical foundations right through to practical implementation.

Aims and Objectives

Module Aims

To gain an in-depth theoretical and practical understanding of modern deep neural networks and their applications.

Learning Outcomes

Knowledge and Understanding

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

  • Underlying mathematical and algorithmic principles of deep learning
  • 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:

  • Critical appraisal of recent scientific literature in deep learning
  • Critically appraise the merits and shortcomings of model architectures on specific problems
Subject Specific Practical Skills

Having successfully completed this module you will be able to:

  • Apply existing deep learning models to real datasets
  • Gain facility in working with deep learning libraries in order to create and evaluate network architectures

Syllabus

Historical Developments - Deep Belief Networks - CNNs, LeNet and the ImageNet competition - RNNs Learning Algorithms - Initialisation - SGD, Momentum, etc. Deep Belief Networks - RBMs Auto-encoders - variational - denoising CNNs - Architectures - Region Propositions - Semantic Segmentation Sequence Modelling - Linear Embeddings - RNNs ∗ LSTMs ∗ GRUs ∗ back-prop through time Deep Learning Technologies Applications - Computer Vision - Natural Language Processing & Generation - Speech

Learning and Teaching

Teaching and learning methods

Lectures, labs and guided self-study

TypeHours
Specialist Laboratory20
Wider reading or practice46
Completion of assessment task60
Lecture24
Total study time150

Resources & Reading list

Ian Goodfellow, Yoshua Bengio and Aaron Courville (2016). Deep Learning. 

Assessment

Summative

MethodPercentage contribution
Final project 40%
In-class task 20%
Lab work 40%

Repeat

MethodPercentage contribution
Assignment 100%

Referral

MethodPercentage contribution
Assignment 100%

Repeat Information

Repeat type: Internal & External

Linked modules

Prerequisites: COMP3206 or COMP3223 or COMP6229 or COMP6245

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