Researchers found ways to keep the correction useful through more layers. Deep networks became more practical to train.

But practical did not mean fast.

Training still meant sending many examples through many layers, calculating an error, working backward through every weight, and repeating the process again and again.

The learning method was improving. Now it needed hardware that could perform a huge amount of ordinary arithmetic much faster.

The next chapter opens a graphics chip and finds exactly that.