Implement Convolutional and Recurrent Neural Networks for Image and Sequence Data

Learning Objectives

At the end of this theory note, trainees must be able to:

1. Explain the fundamentals of convolutional neural networks, including convolution, pooling, and fully connected layers.
2. Identify recognised CNN architectures and their typical use cases.
3. Explain recurrent neural networks and the vanishing gradient problem in sequence modelling.
4. Explain LSTM and GRU cell structures and their advantages for long sequences.
5. Pre-process and augment image datasets using OpenCV and framework utilities.
6. Evaluate CNN and RNN model performance using appropriate metrics.