This paper focuses on feed-forward neural networks. Mimicking brain functions, neural networks are used for prediction and classification. Neural networks are composed of neurons organized in layers and connected by synapses. From the input values through weights on the synapses and neurons in hidden layers, neural networks compute the prediction in the output layer. Utilizing the backpropagation algorithm and learning examples the network is able to learn how to respond to unknown situations. The idea behind backpropagation is to change weights on the synapses until the difference between computed and desired values is small enough. In addition, the work presents functional programming and the OCaml functional programming language. An implementation of a neural network with backpropagation in the OCaml programming language is presented at the end of the work. The work concludes with an application of the neural network to a real-world example, where the error of the network is evaluated on a specific data set.
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