You're right, the code doesn't explicitly define a loss function. However, it implicitly uses mean squared error (MSE) as the loss function during the backpropagation step.

The MSE loss function is defined as:

`L = (1 / N) * Σ(target_i - output_i)^2`

where N is the number of output nodes, target_i is the target value for the i-th output node, and output_i is the actual output value for the i-th output node.

In the train function, the following line calculates the output_errors, which is the difference between the target values and the actual output values:

```javascript
let output_errors = Matrix.subtract(targets, outputs);
```

These output_errors are used to update the weights and biases, effectively minimizing the MSE loss. The code doesn't calculate the actual MSE loss value, but the backpropagation process implicitly optimizes the network to minimize this loss function.

Let me know if there's anything else I can clarify for you.
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