The use of Deep Learning and its applications

According to Geoffrey Hinton , one of the leading researchers in this area, deep learning is a new type of artificial intelligence in which you make the machine learn from its own experience . And possibly the future of unsupervised machine learning as you don't need to have a tagged data set. In this context, algorithms are capable of learning without prior human intervention, drawing conclusions about the data themselves. Marketingmediaweb

The context of Deep Learning

But how does it work? At this point, the use of artificial neural networks comes into play. We remember that an artificial neural network is a set of artificial neurons that are grouped in layers connected to each other and that transmit signals. They are widely used in fields where looking for solutions or characteristics using conventional programming is very difficult, such as computer vision, voice recognition, etc., since they are systems that learn and form on their own, instead of being explicitly programmed . These artificial neural networks are inspired by the biological neural networks that make up animal brains.  techsmartinfo

There are three types of layers: input, hidden and output. The input layers are made up of neurons that receive data or signals from the environment. The output layer is made up of neurons that provide the response of the neural network, and the hidden layers, which can be several, have no direct connection with the environment, that is, they are made up of neurons that have inputs that come from previous layers. and outputs that go to later layers. Divinebeautytips

The interesting thing about these networks is that they are capable of learning in a hierarchical way, that is, information is learned by levels, where the first layers learn very specific concepts, for example, what is a screw, a mirror, a wheel, etc. . And in the later layers previously learned information is used to learn more abstract concepts, for example, a car, a truck or a motorcycle. This means that as we add more layers, the information that is learned is more and more abstract and interesting.

There is no limit to the number of layers that can be added and the trend is that more and more layers are added, becoming increasingly complex networks. This increase in the number of layers and complexity leads us to Deep Learning . Techcrunchblog

How to apply Deep Learning techniques

We can use this technique to a myriad of needs. In the world of online marketing, for example, it helps us to monitor in real time the reactions in online channels during the launch of products, to target ads and predict customer preferences, the probability that a user will click on a call to attention, identify and monitor the levels of customer engagement, their opinions and their attitude in different online channels, among many others.

We also use deep learning techniques in setting up smart translators or in natural language development for virtual assistants. Or for the automation of processes and predictive data analysis.

In advanced image processing, we can detect falling merchandise, work accidents, danger alerts for evacuation, material theft and security of entry and exit of people if it is a sector such as logistics. Also to detect robberies on platforms and stations, collapses of people or medical emergencies, needs that often appear in the transport sector. But it is also applied in the health field, since we can do an analysis of medical images, increasing diagnostic precision in less time and cost. Either for the identification of facial emotions or location of faces.

Application types

In voice recognition, the use of these services is increasingly useful in companies, since precise and fast solutions are achieved. The goal in this field is to get machines to better understand user comments to get more value out of conversations. We can use it to be able to publish on social networks, send emails, search in the browser without the need to write, or to translate texts, locate keywords in reports or documents, among others.

In facial recognition, this development will enhance security in the different services in which personal identification is essential, such as access controls at airports. Nanobiztech

Applied in the prediction of behaviors, we can also incorporate content and present options to people, based on their past preferences, target ads and define audiences, or identify potential customers.

In short, there are many possibilities that can drive the digital transformation of a business. In fact, this technology in companies achieves the combination of strategy and options with a focus on innovation and data analytics, which translates into increased productivity and a more optimal value chain.

Globalmarketingbusiness

 

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