Deep Learning and its many applications

In recent years the popularity and use of algorithms based on Deep Learning has increased due to the multitude of fields in which it is possible to apply them and the good results offered by this solution.

What is Deep Learning use for?

The main objective of the Deep Learning algorithms is to achieve tasks that a human would perform almost automatically but that become complex for a machine. An example would be to be able to detect and identify all the elements of an image. Today, Deep Learning is considered the best image classifier and represents the state of the art in Computer Vision. These algorithms are the most used currently and the main object of research in that field.

What is Deep Learning based on and how does it work?

Deep Learning is a branch of Artificial Intelligence based on the use of artificial neural networks. These neural networks are inspired by the functioning of neurons in the human brain. In the same way that a neuron receives and transmits electrical impulses, an artificial neuron receives information which is transformed and sent to other adjacent neurons. In this way, the information is transformed as it is transmitted by all neurons until it reaches the end of the network. Finally, the network will offer an output with the results it has obtained based on the data it has received as input, of how it has been trained and the purpose for which it is being used.
It is necessary to train the network because it need to learn from the data provided to you. As a network with information is trained, it modifies its neurons until it is able to draw the correct conclusions, even with data that has not been previously provided. For example, if we introduce images into a network indicating which of them are dogs and which are not, the network will end up learning to find dogs in any new image that is provided. In this way, Deep Learning can be applied in various fields with multiple objectives, from reading manuscript texts, to finding out the age of a person based on an image.

In which applications is Deep Learning used?

Currently, Deep Learning is used in a large number of applications that are used on a daily basis, such as the Google translator; in virtual assistants such as Siri, Cortana and Google Assistant, which use Deep Learning algorithms for voice recognition; classification of emails and even for security systems that make use of facial recognition. Another of the areas where Deep Learning is applied, is in something as complex as autonomous cars, which every day are closer to become a reality.
In the case of factories, for example, it can be used to recognize new parts that have not been previously introduced into the system, since the Deep Learning algorithm has ‘studied’ other previous photos in which it has been indicated what it is a piece and when a new part has been introduced into the system, it has been recognized as such without having to indicate it.
Another very important application in factories is the intelligent recognition of defects. Once the system has been trained with different defects (shape, size, geometry …), it is possible that the system could recognize new defects because it has learned what it is. It is a very interesting application because of the variability of defects it is common not to be able to categorize all at first.
As we are seeing, the field of action of Deep Learning is very wide, and it is in Industry 4.0, in particular in Computer Vision, where more possibilities and greater advances are taking place. These algorithms, combined with the Computer Vision allow us to perform tasks such as measuring distances, predicting failures, detecting objects or reading texts. Functions that, until now, required human intervention and for which a great investment of time was necessary, could be automated thanks to Deep Learning.
These are just some of the many applications that Deep Learning offers us today, and that will be many more in the future since it is a field that in full expansion and is increasingly applicable to more fields and is able to solve more complex problems.
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