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Here’s an interesting and skeptical walk through of neural networks vs deep neural networks and what, if anything, makes them different.

Here’s an  excerpt:

The big bang of deep learning – or at least when I heard the boom for the first time – happened in an image recognition project, the ImageNet Large Scale Visual Recognition Challenge, in 2012. In order to recognize images automatically, a convolutional neural network with eight layers – AlexNet – was used. The first five layers were convolutional layers, some of them followed by max-pooling layers, and the last three layers were fully connected layers, all with a non-saturating ReLU activation function. The AlexNet network achieved a top-five error of 15.3%, more than 10.8 percentage points lower than that of the runner up. It was a great accomplishment!

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