As you saw in the previous example, increasing the complexity of the network increases the time and the memory needed to train it. Sometimes, we have to accept that we don't have a machine powerful enough to try all the combinations. What can we do in that situation? Basically, we can do two things:
- Simplify the network; that is, by removing parameters and variables
- Use a pre-trained network, which has already been trained by someone with a powerful enough machine
In both situations, we will work in sub-optimal conditions, since the deep network won't be as powerful as the one we could have used. More specifically, in the first case, the network won't be very accurate because we have fewer parameters; in the second ...
