What is Deep Learning

What is Deep Learning

In simple words about the principle of deep learning of neural networks, what is the difference between machine learning and how to use it?


The terms of deep learning and machine learning often appear in the media or technical publications. Unfortunately, they are often confused as synonyms. But this is not at all the case. Deep learning and Machine learning are two independent areas of artificial intelligence, they have different essence and purpose.

What is artificial intelligence?

It is an academic discipline that emerged in the middle of the 20th century. AI specialists are developing computer systems that can solve problems that were previously considered a human prerogative. Simply put, AI must be able to parse questions, the answers to which require intelligence.

Let’s give a classic example of the development of artificial intelligence – games. Initially, the computer was assigned simple things, like playing checkers or chess. Over time, artificial intelligence mastered more and more complex areas. AI recently defeated the world champions in the game of go, which for several decades was considered inaccessible to the “understanding” of the computer. Also, machines have learned to defeat people in games like Starcraft II, where it is necessary to be able to analyze the situation and calculate actions several steps ahead. Of course, artificial intelligence is also doing well in other fields – economics, logistics, science, medicine … Fewer and fewer blank spots remain on the AI ​​map.

What is machine learning?

Machine learning is a field of artificial intelligence related to the creation of algorithms that can modify themselves with little or no human intervention. The methods developed for working with AI at the start of the industry were not suitable for solving more complex problems. For example, hard-coded algorithms do not work very well with the recognition of images, videos, especially emotions or text.

New methods were required that copy the human learning system. The process moves from simple to complex, as, for example, when teaching a student to read: first letters, syllables, then words, phrases, and already in the final – coherent texts.

Much the same with AI: specialists create machine learning algorithms and provide them with huge amounts of information. Algorithms analyze this data and draw conclusions on the basis of which artificial intelligence is improved. So, if you feed the algorithm with signs of cyber-fraudulent attacks on the infrastructure of banks, the AI ​​will learn from examples and be able to detect attacks on its own.

From individual algorithms to neural networks

But some algorithms are unable to analyze human speech, images, or handwriting input. To train AI to solve such problems, experts create artificial neural networks. These are mathematical models that imitate the biological original – the human brain.

So far, simple neural networks can calculate how many specific objects are in a picture, “recognize” a simple object, for example, distinguish a dog from a cat, etc. But more complex neural networks are capable of solving complex problems that machines could not cope with before. 

For the system to learn to identify, for example, animals, it needs to provide them with labeled images. The more annotated images, the better the system will learn to distinguish cats from dogs. Further, the algorithm will be able to improve the accuracy of the “recognition” of animals. 

Unfortunately, for more complex tasks – real-time human voice recognition, video stream analysis, etc. – this is not enough. AI specialists went further and started working on deep learning of neural networks.

What is the deep learning of neural networks?

Deep learning of neural networks is a new stage in the development of neural network science. In it, networks include many building blocks that interact within more than one layer. Such systems are capable of solving very complex problems.

The success of machines in Go, Starcraft, and other games described above was made possible precisely thanks to deep learning. Such deep neural networks (GNNs) can work with complex images in real-time. For example, a trained multilayer neural network can recognize an aircraft from an unusual angle and against any background. The neural network identifies an object as an airplane, even if it is a toy and, for example, with eyes and clothes.


Here, the input data is sent to different layers of neural networks at the same time, and each looks at the images from their own perspective. The neurons here are formed into three different types of layers: 

  • input layer; 
  • hidden layers;
  • output layer. 

Hidden layers do the calculations.

Deep learning plays a critical role in speech processing. For example, a complex multilayer neural network can solve a problem like this: “My homeland is France, I lived in England and Peru, what language do I speak fluently?” The neural network will determine the list of the most likely languages ​​spoken by the author of the proposal and select French as the most appropriate option.

It is worth noting that deep learning became possible only after the advent of productive computer systems. The same video analysis and recognition is impossible without powerful computers.

What tasks are Machine learning and Deep learning suitable for?

Different types of problems are solved with the help of these disciplines. If we consider a business, then Machine learning is suitable when:

  • business operations need to be automated – user identification, collection and analysis of customer data – and a personalized approach;
  • there is a set of parsed data that needs to be structured and used to train algorithms.

In the case of Deep learning, the conditions are different:

  • huge amounts of data have not been analyzed, and algorithms cannot be trained on their basis;
  • you need to solve problems that are too complex for machine learning. 

It is worth saying that without AI, machine and deep learning, neither robotic vehicles, nor speech recognition, nor even chess programs are possible. Only these technologies make it possible to teach machines to perform certain tasks by processing sets of large amounts of data and identifying patterns and relationships in them.

The initial data always contains the answers needed by specialists from different industries. The main thing is to learn how to find these answers using artificial intelligence technologies. Information still rules the world, and the one with the most productive AI systems gains a competitive advantage. 


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