We already talked about Artificial Intelligence and today we will look into two subjects strictly connected to that discipline, Machine Learning and Deep learning, trying to figure things out. Those terms are often used improperly as synonymous of AI, but, even if they are “relatives” of that computer science, they are not the same. Let’s see in details the differences between Machine Learning and Deep Learning.

What is Machine Learning?

The term Machine Learning refers to a group of methods to train the Artificial Intelligence so that it can perform activities autonomously without them being previously programmed. Thanks to Machine learning, machines are able not only to solve preset problems but also to learn from experiences just like us, by correcting mistakes and taking decisions autonomously. In general, we can say that are mechanisms through which intelligent machines can improve their abilities and performances over time.

We can identify three different kinds of Machine Learning according to their characteristics:

  • With supervision: it provides the machine with a series of information that allow it to understand how to behave, a sort of database of experiences from which it can gain as it should carry out tasks. Experiences provided are already encoded, and the machine should just analyze them and choose the right response to the stimulus given.
  • Without supervision: this learning method is based on results analysis. Not coded information are provided to machines which should organize them in intelligent way so to learn what are the better results in each situation. Compared to the method with supervision it offers more freedom to machines and it has a higher level of complexity.
  • Reinforcement learning: is a learning system which can be defined as a “meritocratic” method, meaning that the machine learns how to act through rewards: for instance, it will be rewarded when it will achieve its goals. It is the most complex learning model of the three.

What is Deep Learning?

Deep Learning, instead, is a part of Machine Learning, a learning approach which takes the human brain function as model of inspiration. It’s a very complex learning method with different levels of education which needs tailored neural networks and a computing power able to support more layers of calculation and analysis. Although it may seem a futuristic technology, Deep Learning is widely used and present in our daily life. An application example are all the images and vocal recognition systems we use everyday through our smartphones.

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