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Intelligence series: Neural Networks and Rail

Neural networks are a form of computing systems inspired by the human brain. Neural networks are often applied to classic machine learning problems, such as pattern recognition, rule learning or unsupervised data classification. 

The key feature of neural networks is the structure: an input layer, output layer, and a variable number of hidden layers (deep learning, a form of machine learning, utilises multiple hidden layers). Each layer is made up of many nodes working in parallel to make a small transformation of the data they receive, before passing it along to the next layer. The connections between the nodes are ‘weighted’, i.e. some are more influential than others. The network modifies the weighting of the connections between nodes based on whether the transformation they have made has brought the network output closer or further from the target.

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