Backpropagation

Initialize all weights to small random numbers.
For each training example Do
1. Input the training example to the network and compute the network outputs
2. For each output unit $k$ calculate its error term $δ_{k}$ $δ_{k} \leftarrow o_{k} (1 - o_{k}) (t_{k} - o_{k})$
3. For each hidden unit $h$ calculate its error term $δ_{h}$ $δ_{h} \leftarrow o_{h} (1 - o_{h}) \sum_{k \in outputs} w_{h, k} δ_{k}$
4. Update each network weight $w_{i, j}$ $w_{i, j} \leftarrow w_{i, j} + Δ w_{i, j}$ where $Δ w_{i, j} = η δ_{j} x_{i, j}$
Goto 2 if termination condition is not met.