Neural networks can grow quite large in size. This has some implications for memory use; however, efficient structures such as sparse matrices mean that we don't generally run into problems fitting a neural network in memory.
Note
The main issue when neural networks grow large is that they take a very long time to compute. In addition, some datasets and neural networks will need to run many epochs of training to get a good fit for the dataset.
The neural network we will train in this chapter takes more than 8 minutes per epoch on my reasonably powerful computer, and we expect to run dozens, potentially hundreds, of epochs. Some larger networks can take hours to train a single epoch. To get the best performance, you may be considering thousands of training cycles.
The scale of neural networks leads to long training times.
One positive is that neural networks are, at their core, full of floating point operations. There are also a large number of operations that can be performed...