Deep learning is an emerging subfield of machine learning. It employs artificial neural network (ANN) algorithms to process data, derive patterns or to develop abstractions, simulating the thinking process of a biological brain. And those ANNs usually contain more than one hidden layer, which is how deep learning got its name—machine learning with stacked neural networks. Going beyond shallow ANNs (usually with only one hidden layer), a deep learning model with the right architectures and parameters can better represent complex non-linear relationships.

Here is an example of a shallow ANN:

And an example of a deep learning model:

Don't feel scared, regardless of how complicated it might sound or look. We will be going from shallow to deep dives into deep learning throughout five projects in this book.

First of all, as a part of the broad family of machine learning, deep learning can be used in supervised learning, semi-supervised learning, as well as unsupervised learning tasks, even reinforcement learning tasks. So what sets it apart from traditional machine learning algorithms?