Introduction to neural networks by Annelies Gerber & Timothy Flack

An introduction to neural networks with a brief history is given. Traditional machine learning (ML) techniques are highlighted and compared with neural networks (NN).

An overview of the constituent parts of a neural network is given( a node, layer, activation function, loss function, optimiser) is given with a summary to illustrate the neural network architecture. A Python example for movie review classification is then presented. Neural networks with memory (RNN, LSTM) are introduced briefly. Then, convolutional neural networks (CNN) are introduced with their building blocks (convolutional operation, padding, pooling, border effects) and an example is given using image classification (cats vs dogs).


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