Error correction is used to correct any errors that may arise when sending any message, as errors are prone to appear due to noise. For classical codes, there are many decoders used, for example, belief propagation. Unfortunately, these classical decoders are less efficient for quantum codes. A proposed remedy to improve the decoders efficiency is to use a neural network for decoding. In this thesis, we will implement belief propagation on the toric code and check its efficiency. We will see that for few errors, belief propagation works, but it fails for other cases. We also introduce the modifications needed for neural belief propagation, a modification of the original algorithm that integrates a neural network on the algorithm structure.