The example uses the Mean Squared Error (MSE) loss function. This can be inferred from the lines of code that compute the output errors:

    js

    let targets = Matrix.fromArray(target_array);
    let output_errors = Matrix.subtract(targets, outputs);

The squared error is then computed for each output neuron:

    js

    output_errors.map(square);

And the mean of the squared errors is returned as the final error:

    js

    return output_errors.sum() / output_errors.rows;

MSE is a common loss function used in regression problems, where the goal is to predict continuous values. If the problem is a classification problem, a cross-entropy loss function may be more appropriate.
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