Deep neural networks

Now that we have a rich number of layers, it's time to start a tour of how the neural architectures have evolved over time. Starting in 2012, a rapid succession of new and increasingly powerful combinations of layers began, and it has been unstoppable. This new set of architectures adopted the term deep learning, and we can approximately define them as complex neural architectures that involve at least three layers. They also tend to include more advanced layers than the Single Layer Perceptrons, like convolutional ones.

Deep convolutional network architectures through time

Deep learning architectures date from 20 years ago and have evolved, guided for the most part by the challenge of solving the human vision problem. Let's have a look at the main deep learning architectures and their principal building blocks, which we can then reuse for our own purposes.

Lenet 5

As we saw in the historical introduction of the convolutional neural networks, convolutional layers were discovered...