Chapter Summary

    The rate of error for a neural network is a very important statistic, which is used as a part of the training process. This chapter showed you how to calculate the output error for an individual training set element, as well as how to calculate the RMS error for the entire training set.

    Training occurs when the weights of the synapse are modified to produce a more suitable output. Unsupervised training occurs when the neural network is left to determine the correct responses. Supervised training occurs when the neural network is provided with training data and anticipated outputs. Hebb’s rule can be used for unsupervised training. The delta rule is used for supervised training.

    In this chapter we learned how a machine learns through the modification of the weights associated with the connections between neurons. This chapter introduced the basic concepts of how a machine learns. Backpropagation is a more advanced form of the delta rule, which was introduced in this chapter. In the next chapter we will explore backpropagation and see how the neural network class implements it.