Have you ever heard about Deep Learning, Machine Learning, Artificial Intelligence, or that machines will dominate the world?
Yeah, robots like the Terminator may be closer to our reality than you think! But don’t be afraid!
This is a theme on the rise in the technical areas of technology study and our daily life, which is why we must always be informed and aware of the constant innovations of this vast universe.
What Is Deep Learning?
Deep Learning, or Deep Learning, translated into Portuguese, is one of the branches of Machine Learning; it is a machine learning that trains computers to perform human tasks naturally from artificial neural networks.
This is a topic on the rise within Artificial Intelligence (AI) and has allowed analysis of large volumes of complex data from Big Data, rapid identification of problems, and optimization of decisions.
Instead of using algorithms with predefined equations, deep Learning adjusts basic parameters so that the computer can learn by recognizing patterns in several layers of processing.
Did you know that this technology is also present in your daily life?
Neural networks are capable of processing a large amount of data. Based on our online actions, they can learn about our purchase preferences, series, and music and make better suggestions on platforms such as Amazon, Spotify, and Netflix.
Why Use Deep Learning?
With computing advances, access to the cloud, and better processing units, we produce much more data than ten years ago. Analyzing this data so that it becomes our allies is a high-performance task.
The artificial neural networks implemented in the learning layers of Deep Learning can examine this information more effectively and efficiently than traditional methods. See some of the advantages of the technique.
Efficiency
With deep Learning, neural networks can process and optimize high data rates efficiently and much faster. Time spent on modeling, engineering resources, and data processing is significantly reduced.
Assertiveness
Deep Learning performs its Learning through pattern recognition, analyzing a high amount of information. It is capable of correcting and improving itself, thus achieving super high success rates, with error rates below 10-5. That means having one error in every 100,000 samples!
This can be compared to the Sigma quality level. In this case, it is possible to achieve assertiveness capable of fitting into the 6 Sigma level (3.4 defects per million) of the Six Sigma Methodology.
Understanding Of Customers
We generate a database of our preferences with each click we make. Deep Learning allows large companies, which already see technology as an ally, to process this data to customize service and customer service. It is possible to know what to suggest to customers, especially when.
Failure Prediction
Processing complex data efficiently and assertively, learning and adapting autonomously, Deep Learning allows for automating processes and predicting failure patterns. Knowing when an error will occur in your production is an excellent ally in market competitiveness and reducing production costs.
Now that you know the benefits of Deep Learning let’s see some examples of applications.
Examples Of Applications In Everyday Life
Are we too far from the Machine Revolution, or that we have privacy on the net?
I’m sorry to inform you that no, but that’s not necessarily a bad thing.
See some examples of how Deep Learning is present in our daily lives, often helping us without our noticing.
- Security: The facial recognition we use to unlock our cell phones is one of the main functions of Deep Learning. It has been essential for the development of remote devices and also for police use in fugitive and wanted recognition.
- Voice assistants: Assistants like Alexa and a are purely developed with Deep Learning. Its ability to pick up on our preferences, detect our voices and understand our requests are results of Deep Learning.
- Health: Just as Lean Healthcare impacts reducing waste and quality in Healthcare, systems developed with Deep Learning have helped doctors in detecting diseases such as retinas affected by diabetes and detecting cancer cells.
In addition, platforms like Netflix and Spotify can also analyze your content preferences for future suggestions, such as series and playlists.
Artificial Neural Networks
Artificial Neural Networks are computational models analogous to the human brain’s neural connections capable of performing machine learning, Machine Learning, and pattern recognition (Pattern Recognition).
These models consist of systems of connected neurons. They are composed of an input layer to receive the signal. This output layer decides the input, and between these two, an arbitrary number of hidden layers are the exact computational mechanism of the network. With a hidden layer, it is possible to approximate any continuous function.
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