Machine Learning and Deep Learning are concepts you should be familiar with in this era dominated by artificial intelligence. Both are very important to understand and master it, putting your company ahead of the competition and making the best of opportunities that present themselves in the world of technology.
Machine Learning and Deep Learning are forms of artificial intelligence that allow machines to interact more efficiently with humans.
What is Machine Learning?
Machine Learning (or machine learning) is a form of artificial intelligence that allows software applications to be more accurate. The idea is that they can offer alternatives and solutions even without being programmed to do so, as they are able to learn from the information that is fed to them over time.
Therefore, the basic premise of Machine Learning is to create algorithms that learn to read and understand new data, using statistical analysis to determine responses within a finite number of possibilities. There are two types of Machine Learning algorithms out there: supervised and unsupervised.
In the first type of Machine Learning, the presence of humans is required to submit information to the machines and offer feedback regarding their results. In the second, everything is done autonomously by the systems and the results are evaluated by the system itself.
When Machine Learning engines are autonomous, or unsupervised, they often use a feature known as Deep Learning to review the information received and reach conclusions. You will understand how this feature works next.
What is Deep Learning?
Deep Learning is another aspect of artificial intelligence. This feature exactly simulates the way of learning used by humans when it comes to understanding new information and gives machines the ability to do the same.
Therefore, in a simplistic way, Deep Learning (or deep learning) can be seen as a way to automate predictive analytics. This is because while machine learning engines are generally linear, deep learning engines are not. They are chained hierarchically, allowing for more complex and abstract analysis.
The result of this hierarchical chaining is a bunch of non-linear transformations that configure themselves in statistical models. As the system collects a portion of them, it is able to better understand what is happening around it.
What is the difference between Machine Learning and Deep Learning?
The difference between Machine Learning and Deep Learning, therefore, is not difficult to understand. Deep Learning is effectively a type of Machine Learning, but it is not the only one that exists, nor the most traditional.
When we go back to basic assumptions, Machine Learning is the whole practice of using algorithms to understand data. Deep Learning, on the other hand, is the practice of using only algorithms to do this, without the supervision of any kind of human agent.