One of the Artificial Intelligence (AI) innovations that has transformed the industry and how machines operate is deep learning. Our daily lives have improved because to this breakthrough, which paves the way for improved interactions with machines and services in the future.
With the popularization of the concept of Artificial Intelligence, some terms became better known to the general public, provoking questions such as: what is Machine Learning and Deep Learning? To learn more about these concepts and how they apply in society, follow this article!
What is deep learning?
The Deep Learning concept is based on a technology called neural networks, which tries to imitate the behavior of the human brain to understand the information that the system captures, generating dynamic results with this data.
Neural networks are chain layers that act in a hierarchical way in the processing of information, which allows a larger and more complex analysis of each data.
Thus, networks resemble the functioning of neurons in the human brain, going from an input layer to an output layer, conducting information. For data to go from one layer to another, boundary values defined according to the programming used in that network are used.
In this way, it is possible to understand information such as images, sounds and behavioral data without human interference and very quickly and efficiently. But, for you to understand the definition of Deep Learning and how it works, it is necessary to know what Machine Learning and Artificial Intelligence is. Let’s go!
What is Machine Learning and how does this concept relate to Artificial Intelligence?
Artificial Intelligence seeks to develop machines and systems that perform activities that only humans could perform — or, at least, come close to it. The concept emerged in the 1950s and brought together experts from different areas until we reached the current context.
The database of an AI (acronym for Artificial Intelligence) needs to be large, in addition to being fed uninterruptedly so that it can develop. And Machine Learning starts from that principle.
Machine Learning is, in free translation, machine learning. It uses algorithms to gather and organize data, detecting different patterns and generating intelligent solutions for the area in which it was programmed, therefore following logic of instructions.
How do they integrate?
If Deep Learning and Machine Learning are concepts that are part of Artificial Intelligence, how do they integrate? In practice, the first is an evolution of Machine Learning, since it is based on neural networks, while ML is linear.
Because it is linear, machines learn from data without programming, but they do not have the assimilation capacity that Deep Learning offers, since it recognizes and executes several tasks simultaneously, based on the data it captures.
Deep Learning brings data capable of improving Machine Learning functions, recognizing and processing insights that make technologies continue to evolve.
Deep Learning Applications
Currently, Deep Learning applications are closer to you than you think. There are several innovations that use technology to create solutions used in our daily lives. Let’s go to them!
1. Natural language processing
Speech recognition, which is heavily used by browsers and personal assistants, is a technology acquired by the model. We can observe a continuous growth of technology in recent years, being applied in other sectors and with different uses.
2. Diagnostic imaging
The healthcare area has benefited greatly from diagnostic imaging, in which diseases are identified much more quickly and efficiently, using image comparison in a constantly updated database.
3. Fraud detection
The standardization of information and data promotes greater security, which reduces the possibility of fraud in companies that use algorithms in their processes. In this way, when identifying fraudsters, their patterns and scams are also assimilated by technology, which acts preventively.
4. Sentiment analysis
Many brands and private institutions that sell advertising spaces use machine learning to identify what users feel when viewing such content, managing to detect what really works according to their strategy.
5. Recommendation systems
Widely used in the advertising market and also in delivery applications, for example, recommender systems have evolved thanks to Deep Learning. The intention is to capture the tastes of each user according to their searches, purchases and clicks, indicating the ideal products for each one.
6. Computer vision
Autonomous cars, increasingly common abroad, use computer vision to identify objects and scenarios in real time, processing all information seen intelligently and quickly.
This skill is also applied in health, in CT scans, MRIs and other exams. The military sector also makes use of computer vision in anti-missile systems, for example.
7. Failure prediction
With Deep Learning, it is possible to predict failures in various equipment, with virtual tests and also based on user experiences, bringing a breakthrough in the identification of these defects.
Advances also occur in virtual protection, in software such as spam filtering, network intrusions and other digital problems that may occur.
