I'm surprised that the article doesn't mention that one of the key factors that enabled deep learning was the use of RELU as the activation function in the early 2010s. RELU behaves a lot better than the logistic sigmoid that we used until then.
Our labeled datasets were thousands of times too small.
Our computers were millions of times too slow.
We initialized the weights in a stupid way.
We used the wrong type of non-linearity.nets too small (not enough layers)
gradients not flowing (residual connections)
layer outputs not normalized
training algorithms and procedures not optimal (Adam, warm-up, etc)