We indicated in the concluding remarks of the previous chapter that feedforward neural networks have powerful modeling capabilities, as reflected by the universal approximation theorem. In one of its versions, the theorem asserts that networks with a single hidden layer are rich enough to model almost any arbitrary function.
Review the options below to login to check your access.
Log in with your Cambridge Higher Education account to check access.
If you believe you should have access to this content, please contact your institutional librarian or consult our FAQ page for further information about accessing our content.