Course overview
This course will provide students with an advanced understanding of deep learning applications by exploring the theory and practical implementation of deep neural networks.
Course learning outcomes
- Explain the fundamental concepts and theories of deep learning, including the architecture and functioning of neural networks
- Design, implement, and train convolutional neural networks (CNNs) for image processing and classification tasks
- Develop recurrent neural networks (RNNs) and their variants (LSTM, GRU) for sequential data processing and natural language processing applications
- Apply advanced deep learning techniques such as transfer learning, generative adversarial networks (GANs), and reinforcement learning to enhance model performance
- Evaluate the performance of deep learning models using appropriate metrics and techniques, and optimise models for improved accuracy and efficiency
- Integrate and apply deep learning knowledge and skills in a comprehensive capstone project, demonstrating the ability to solve complex real-world problems
Degree list
The following degrees include this course