Computing power, but also some implementation tricks that turn out to make things a lot better. For example, activation via ReLU instead of sigmoid/tanh signif

Computing power, but also some implementation tricks that turn out to make things a lot better. For example, activation via ReLU instead of sigmoid/tanh significantly improves the performance of deep neural networks. Then there's stuff like BatchNorm, Pooling, Dropout etc...