Gradients

Even with transparent GPU support, all of this dancing with tensors isn’t worth bothering without one “killer feature” — the automatic computation of gradients. This functionality was originally implemented in the Caffe toolkit and then became the de facto standard in DL libraries.

Earlier, computing gradients manually was extremely painful to implement and debug, even for the simplest neural network (NN). You had to calculate derivatives for all your functions, apply the chain rule, and then implement the result of the calculations, praying that everything was done right. This could be a very useful exercise for understanding the nuts and bolts of DL, but it wasn’t something that you wanted to repeat over and over again by experimenting with different NN architectures.

Luckily, those days have gone now, much like programming your hardware using a soldering iron and vacuum tubes! Now, defining an NN of hundreds of layers...