The objective of the class is to give an introduction to Deep Learning techniques.  At the end of the class, students will:

• have an understanding of neural network architectures including convolutional and recurrent networks.
  
• learn and understand the methods for how to efficiently train and evaluate neural networks including loss functions, backpropagation, regularisation, etc.
  
• understand the benefits of depths and representation learning.
  
• gain the ability to build and train neural network models in a Deep Learning Framework such as TensorFlow / Keras or PyTorch.