Now that we've defined how we will structure data and refreshed ourselves on bias and variance, let's consider how we will control bias and variance errors in our deep neural networks.
- High bias: A network with high bias will have a very high error rate when predicting on the training set. The model is not doing well at fitting the data. In order to reduce the bias you will likely need to change the network architecture. You may need to add layers, neurons, or both. It may be that your problem is better solved using a convolutional or recurrent network.
Of course, sometimes a problem is high bias because of a lack of signal or very difficult problem, so be sure to calibrate your expectations ...
