What is Deep Learning and How Does It Work?

Deep Learning is a form of Artificial Intelligence, derived from Machine Learning. To understand what Deep Learning is, it is important to understand what Machine Learning is.

The Machine Learning concept dates back to the middle of the 20th century. In the 1950s, the British mathematician Alan Turing imagined a machine capable of learning, a “Learning Machine”. Over the next few decades, different Machine Learning techniques were developed to create algorithms that could learn and improve independently.

These techniques include artificial neural networks. These algorithms are the basis of Deep Learning, but also technologies such as image recognition or robotic vision. Artificial neural networks are inspired by the neurons of the human brain. They consist of several artificial neurons connected to each other. The higher the number of neurons, the deeper the network is.

In the human brain, each neuron receives about 100,000 electrical signals from other neurons. Each active neuron can produce an exciting or inhibiting effect on those to which it is connected. In an artificial network, the principle is similar. Signals travel between neurons. However, instead of an electrical signal, the neural network assigns some weight to different neurons. A neuron that receives more charge will exert more effect on adjacent neurons. The final layer of neurons responds to these signals.

To understand how Deep Learning works, let’s take a concrete example of image recognition. Imagine that the neural network is used to recognize photos that contain at least one cat. In order to be able to identify the cats in the photos, the algorithm must be able to distinguish the different types of cats, and to recognize a cat in a precise way whatever the angle under which it is photographed.

In order to achieve this, the neural network must be trained. To do this, it is necessary to compile a set of training images to practice Deep Learning. This set will gather thousands of pictures of different cats, mixed with images of objects that are not cats. These images are then converted into data and transferred to the network. Artificial neurons then assign a weight to the different elements. The final layer of neurons will then gather the different information to deduce whether or not it is a cat.

The neural network will then compare this response to the correct answers given by humans. If the answers match, the network keeps this success in memory and will use it later to recognize cats. In the opposite case, the network takes note of its error and adjusts the weight placed on the different neurons to correct its error. The process is repeated thousands of times until the network is able to recognize a cat on a photo in all circumstances. This learning technique is called supervised learning.

Another learning technique is unsupervised learning. This technique relies on data that is not labelled.