What is deep learning?
Posted: Tue Jan 21, 2025 6:17 am
The castle of artificial intelligence is made up of many components, including deep learning, a discipline that attempts to emulate human behavior. The literal translation is deep learning, and it is a subcategory of machine learning that, however, involves something much broader than simple multi-level machine learning. Let's try to understand what deep learning is, how it works, and what kind of applications it can have .
What is deep learning?
Deep learning refers to that branch of artificial intelligence that refers to algorithms inspired by the structure and function of the brain , the so-called artificial neural networks. These algorithms are designed to imitate the human brain, they must be trained using large data sets so that they learn patterns (even complex ones) and can generate accurate predictions.
Therefore, a key concept to understand what deep learning is is that of an artificial neural network , a system that uses algorithms to recognize relationships between data just as a human brain would do when evaluating and creating relationships with the environment. In other words, just as our brain is also responsible for understanding the environment and its changes by providing appropriate responses to contingent needs, artificial neural networks use data sets to derive identifying traits.
From a scientific point of view, one could say that deep jamaica whatsapp data learning represents machine learning by processing learned data using mainly statistical computer algorithms.
How does deep learning work?
Now that we understand what deep learning is, let’s try to see how it works . Moving away from the technical-technological aspects, we must think of deep learning as an assembly line where a mechanical arm processes a product that has already been processed by the previous arm and hands it over to the next arm. For example, one arm shapes a screw that the next arm screws on before passing it on to the next arm along the same assembly line. The final product, a precision screw, is the result of all the production steps.
Similarly, deep learning extrapolates information from the data it is fed , analyzing and classifying it, reaching the point of finding models (patterns) within the data itself. Finally, deep learning can be summarized as a discipline through which a system learns to perform complex tasks thanks to the analysis of large amounts of data.
What is deep learning used for?
By applying deep learning, we will therefore obtain a machine capable of autonomously classifying data and structuring it hierarchically , finding the most relevant and useful ones to solve a problem (exactly as the human mind does), improving its performance with continuous learning. That said, it is clear that it is not enough to understand what deep learning is to know how to apply it.
As you can imagine, there is a lot of potential and this is therefore a sector of great strategic importance that requires professionals trained in Data Science and who have completed, for example, a Master's in Big Data & Analytics , but also a more generic Master's in STEM Management , which will then be completed with field experiences.
Deep learning networks have many advantages , but also some disadvantages. Among the advantages we find:
They adapt easily to any situation. They can be used for both relatively simple tasks (classifying information) and complex tasks.
If properly trained (i.e. with lots of data), they deliver results quickly and reliably .
Among the main disadvantages :
They are subject to the phenomenon commonly known as the 'black box' , that is, it is not known why or how a neural network decides to give a certain result. For example, when a neural network is faced with a photograph of a cat and says that it is a seagull, it is difficult to understand how and why it arrived at this classification.
Deep learning neural network models require much more data than machine learning models . For example, for a neural network to recognize an image of a watch on a man's wrist, it must first recognize a wristwatch, an arm, a man, and then a wristwatch on a man's arm.
Examples of deep learning
Despite these problems, deep learning systems have made great and important evolutionary steps and have improved greatly in recent years, due to the enormous amount of data available but, above all, to the availability of ultra-performance infrastructures (CPUs and GPUs in particular).
In the context of artificial intelligence research, machine learning has enjoyed considerable success in recent years, allowing computers to surpass or approach human performance in areas ranging from facial recognition to speech and language. Deep learning, however, allows computers to go a step further in solving complex problems such as those involving quantum computing.
That said, deep learning is already here. Natural language recognition, commonly used in speech recognition and synthesis software for chatbots and service robots, is perhaps the most common representation of deep learning application that is already within everyone’s reach.
However, without being technology experts, it is already possible to list several use cases to better understand what deep learning is for , including:
computer vision for driverless vehicles ;
Robotic drones used for package delivery or emergency assistance (e.g. for delivering food or medicine in crisis areas);
facial recognition for surveillance;
image recognition for diagnostic , radiological or for the identification of genetic sequences or pharmaceutical molecules;
It is used in scientific disciplines in general, from medicine to astronomy and research;
analysis systems for predictive maintenance of an infrastructure or plant by analyzing data from IoT sensors;
in machine translation ;
In sentiment analysis , a tool used to understand what the public thinks about a product or service, for example by scanning online reviews written by customers.
What is deep learning?
Deep learning refers to that branch of artificial intelligence that refers to algorithms inspired by the structure and function of the brain , the so-called artificial neural networks. These algorithms are designed to imitate the human brain, they must be trained using large data sets so that they learn patterns (even complex ones) and can generate accurate predictions.
Therefore, a key concept to understand what deep learning is is that of an artificial neural network , a system that uses algorithms to recognize relationships between data just as a human brain would do when evaluating and creating relationships with the environment. In other words, just as our brain is also responsible for understanding the environment and its changes by providing appropriate responses to contingent needs, artificial neural networks use data sets to derive identifying traits.
From a scientific point of view, one could say that deep jamaica whatsapp data learning represents machine learning by processing learned data using mainly statistical computer algorithms.
How does deep learning work?
Now that we understand what deep learning is, let’s try to see how it works . Moving away from the technical-technological aspects, we must think of deep learning as an assembly line where a mechanical arm processes a product that has already been processed by the previous arm and hands it over to the next arm. For example, one arm shapes a screw that the next arm screws on before passing it on to the next arm along the same assembly line. The final product, a precision screw, is the result of all the production steps.
Similarly, deep learning extrapolates information from the data it is fed , analyzing and classifying it, reaching the point of finding models (patterns) within the data itself. Finally, deep learning can be summarized as a discipline through which a system learns to perform complex tasks thanks to the analysis of large amounts of data.
What is deep learning used for?
By applying deep learning, we will therefore obtain a machine capable of autonomously classifying data and structuring it hierarchically , finding the most relevant and useful ones to solve a problem (exactly as the human mind does), improving its performance with continuous learning. That said, it is clear that it is not enough to understand what deep learning is to know how to apply it.
As you can imagine, there is a lot of potential and this is therefore a sector of great strategic importance that requires professionals trained in Data Science and who have completed, for example, a Master's in Big Data & Analytics , but also a more generic Master's in STEM Management , which will then be completed with field experiences.
Deep learning networks have many advantages , but also some disadvantages. Among the advantages we find:
They adapt easily to any situation. They can be used for both relatively simple tasks (classifying information) and complex tasks.
If properly trained (i.e. with lots of data), they deliver results quickly and reliably .
Among the main disadvantages :
They are subject to the phenomenon commonly known as the 'black box' , that is, it is not known why or how a neural network decides to give a certain result. For example, when a neural network is faced with a photograph of a cat and says that it is a seagull, it is difficult to understand how and why it arrived at this classification.
Deep learning neural network models require much more data than machine learning models . For example, for a neural network to recognize an image of a watch on a man's wrist, it must first recognize a wristwatch, an arm, a man, and then a wristwatch on a man's arm.
Examples of deep learning
Despite these problems, deep learning systems have made great and important evolutionary steps and have improved greatly in recent years, due to the enormous amount of data available but, above all, to the availability of ultra-performance infrastructures (CPUs and GPUs in particular).
In the context of artificial intelligence research, machine learning has enjoyed considerable success in recent years, allowing computers to surpass or approach human performance in areas ranging from facial recognition to speech and language. Deep learning, however, allows computers to go a step further in solving complex problems such as those involving quantum computing.
That said, deep learning is already here. Natural language recognition, commonly used in speech recognition and synthesis software for chatbots and service robots, is perhaps the most common representation of deep learning application that is already within everyone’s reach.
However, without being technology experts, it is already possible to list several use cases to better understand what deep learning is for , including:
computer vision for driverless vehicles ;
Robotic drones used for package delivery or emergency assistance (e.g. for delivering food or medicine in crisis areas);
facial recognition for surveillance;
image recognition for diagnostic , radiological or for the identification of genetic sequences or pharmaceutical molecules;
It is used in scientific disciplines in general, from medicine to astronomy and research;
analysis systems for predictive maintenance of an infrastructure or plant by analyzing data from IoT sensors;
in machine translation ;
In sentiment analysis , a tool used to understand what the public thinks about a product or service, for example by scanning online reviews written by customers.