As Mónica Villas pointed out, “Artificial Intelligence is not the future, but the present”. AI is a branch within research and computer science. Through logical-mathematical mechanisms, machines are programmed to satisfy people's needs.
In recent times, we've seen that there are different types of Artificial Intelligence. Today we'll delve into machine learning and deep learning.
In this article, we are going to look at the differences between machine learning and deep learning And what do these two branches of machine learning consist of.
AI: The meeting point of machine learning and deep learning
Artificial Intelligence is a very powerful science, which encompasses different types of technology. Without going any further, we can observe different paradigms that are divided into 2 major groupings: Robust Artificial Intelligence and Applied Artificial Intelligence.
- IA Robusta (Strong AI) This idea would involve machines having an intelligence similar to human cognitive ability.
- Applied AI (Weak AI) In this second section, we observe those machines whose learning is guided through algorithms. Indeed, machine learning and deep learning would fall under this category.
That being said, let's look at the differences between machine learning and deep learning, and their definitions.
Machine learning is a subfield of artificial intelligence that allows computer systems to learn from data without being explicitly programmed. It uses algorithms to identify patterns and make predictions or decisions based on the data it has been trained on.
First of all, machine learning refers to the use of mathematical algorithms to enable machines to imitate the way humans learn.
Through these algorithms, machines analyse and take a series of data and, consequently, learn to make decisions based on what they have learned. That is to say, they acquire a series of patterns, thanks to a programming previous.
What is deep learning?
For its part, Deep learning is a part of machine learning itself.. It is very similar to the first, but employs different algorithms:
While decision algorithms (decision trees) are used in machine learning, neural networks that mimic the neural networks of the human brain are employed in deep learning.
If you're wondering about the differences between machine learning and deep learning, we can say that the latter is a branch of the former. Deep learning goes beyond machine learning, as it attempts to emulate human learning.
In this way, deep learning is a more detailed, more evolved type of learning; therefore it is more accurate and its margin of error much smaller.
When to use deep learning? And machine learning?
You already know the differences between machine learning and deep learning, But do you know where to apply this type of technology?
The artificial intelligence It is present in our day-to-day. At home, at work, or in any other aspect of life. In this way, let's look at some examples of artificial intelligence where both deep learning and machine learning can be applied:
- Home automation Or robotics in the home. From a smart TV, to a mirror, to the lighting itself… Thanks to AI, your home can also be smart.
- Voice recognition: Voice assistants, such as Siri, Alexa, or Google Assistant, work with deep learning.
- Search engines This is certainly the case with Google, which aims to personalise and tailor its services to each user.
- Bots Or customer service chats. These aim for natural language, answering customers' most frequent questions.
- Data Prediction Machine learning employs various predictive models, which can be applied to different fields of knowledge. In medicine, for example, to inquire about a disease, or in economics, when predicting the prices of products or services.
Do you want to work in AI?
Have you understood the differences between machine learning and deep learning? Do you find the field of Artificial Intelligence interesting enough to pursue professionally? If so, At IMMUNE, we have a training offer designed for you.
Be sure to check out our Data Science Master, also available in online version; as well as this Voice Tech Bootcamp, specialising in that voice recognition by virtual assistants.
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