# Implementing a Multilayer Perceptron for the XOR Problem

Learn how to implement a basic multilayer perceptron and what to expect from it.

## Naming, initializations, and error calculation

Let’s illustrate a basic multilayer perceptron implementation in Python on the $\text{XOR}$ problem. As already discussed in the simple perceptron implementation before, the program starts by defining the training problem (the training dataset) in the feature arrays `X`

and desired label vector `Y`

. We then introduce and initialize some variables, which now include the activation of the hidden nodes `h`

and the weights to the hidden nodes `wh`

, as well as the corresponding gradient `dwh`

and delta term `dh`

.

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