Deep Neural Network (Software Engineering Programme)
This course will aim to introduce students to the core fundamentals of modern deep multi-layered neural networks, while still remaining grounded in practice. The underpinning assumption in its design is that while students may have experience (especially hands-on experience) in machine learning, data science or general software engineering — they have not worked with deep learning or taken prior courses in the area.
At the conclusion of this course students should understand:
- The principles and approaches for learning with deep neural networks.
- The main variants of deep learning (such convolutional and recurrent architectures), and their typical applications.
- The key concepts, issues and practices when training and modeling with deep architectures; as well as have hands-on experience in using deep learning frameworks for this purpose.
- How to implement basic versions of some of the core deep network algorithms (such as back-propagation)
- How deep learning fits within the context of other machine learning approaches, and what tasks it is considered to be suited and not well suited to perform.
Where: Department of Computer Science, Oxford University.
Next Iterations: 21/11/2022.
More Details: SEP-Deep Neural Networks