Deep learning

In the last section, we saw how a number of perceptrons can be stacked together in multiple layers to start a learning network. We saw an example of a feed forward network with just one hidden layer. Apart from just a single hidden layer, we can have multiple hidden layers stacked one after the other. This would enhance the accuracy of the artificial neural network further. When an artificial neural network has multiple hidden layers (that is, greater than one), this approach is called deep learning as the network is deep.

Deep learning is currently one of the most widely studied research topics and it is practically used in many real-world applications.

Let's now see some of the advantages and real-world use cases of deep learning.

Advantages and use cases of deep learning

There are two main advantages of deep learning:

1. No feature engineering required: In traditional machine learning, feature engineering is of the utmost importance if you want your models to work well. There...