Abstract
Neural networks are complex algorithms that can be used for supervised classification and regression problems, as well as unsupervised models. Supervised neural networks take in the input data through input nodes (neurons), pass it through hidden layer node(s) while assigning weights to each node, and then represent it through the output layer. Various factors determine the structure of a neural network and its output, including but not limited to the number of hidden layers, number of nodes in each hidden layer, and activation function. In this chapter, we will briefly introduce some of the supervised and unsupervised neural networks algorithms, and compare effects of changing model parameters on its performance.
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