During the training of the neural network, the model may undergo various symptoms. One of them is high bias value. This leads to a high error rate on our training dataset and therefore, consecutively, a similar error on the development dataset. What this tells us is that our network has not learned how to solve the problem or to find the pattern. Graphically, we can represent the model like this:

This graph depicts senior boundaries and the errors caused by the model, where it marks the red dots as green squares and vice versa.

We may also have to worry about the high variance problem. Assume that the neural network does a great job during the training phase and figures out a really complex decision bundle, almost perfectly. But, when the same model uses the development testing dataset, it performs poorly, having a high error rate and an output that is not different from the high bias error graph: 

If we look at the previous graph, it looks like a neural network really learned to be specific to the training dataset, and when it encounters examples it hasn't seen before, it doesn't know how to categorize our data.

The final unfortunate case is where our neural network may have both of these two symptoms together. This is a case where we see a high error rate on the training dataset and a double or higher error rate for the development dataset. This can be depicted graphically as follows: 

The first line is the decision boundary from the training dataset, and the second line is the decision boundary for the development dataset, which is even worse.