Deep learning is an approach to machine learning developed over the past several decades that draws heavily on our knowledge of the human brain, statistics and applied mathematics. In recent years, deep learning has grown tremendously in popularity and usefulness, largely due to more powerful computers, larger datasets and techniques to train deeper networks.
You will look into modern deep neural networks starting from the fundamentals of Artificial Neural Networks (ANNs). You will start by learning what is an artificial neuron and architectures like multilayer perceptron, expanding on algorithms for training ANNs such as gradient descent and backpropagation.
You will examine applications of deep learning with particular attention to coding aspects during lectures and practical activities. You will learn to use the most popular techniques to evaluate the performance of a model on real data. For this, you will be introduced to different types of deep neural networks, from convolutional networks to recursive neural networks, and autoencoders to address different problems with a particular focus on computer vision topics.
Lectures and classes: 38
Individual Mini project and 5 minutes individual presentation -60%
Computer-Based, Invigilated Mini Practical-40%
Reassessment
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On successfully completing the module, students will be able to:
1) Critically interpret and apply the theory, implementation, training and testing of deep learning architectures.
2) Critically evaluate computational and practical challenges existing in the various applications of deep learning architectures on real-world data.
3) Deploy accurately deep learning architectures and write software to solve practical problems.
4) Explain and construct reasoned arguments about the pros and cons of deep learning algorithms and their implementations.
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