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 on a reasonable rate (I like to start by calibrating on human accuracy).

• High variance: A network with a low bias error is fitting the training data well; however, if the validation error is greater than the test error the network has begun to overfit the training data. The two best ways to reduce variance are by adding data and adding regularization to the network. 

Adding data is straightforward but not always possible. Throughout the book, we will cover regularization techniques as they apply. The most common regularization techniques we will talk about are L2 regularization, dropout, and batch normalization.