Deep Learning: what is the essence of the deep learning method?

In this article, we will look at how deep learning, neural connections work in comparison with machine learning and talk about how to apply deep learning in practice.

The terms "Machine learning" and "Deep learning" are often considered synonymous, which is a misconception. Both terms can be found in the media or technical articles, but it is important to understand that these are two separate areas of artificial intelligence, each of which has its own meaning and meaning. Before proceeding to their detailed study, let us consider the meaning of the term "Artificial Intelligence".

Deep Learning: what is the essence of the deep learning method

Briefly about artificial intelligence

In the middle of the 20th century, specialists began to develop computer systems that are capable of solving various problems and issues. Previously it was believed that only a person is capable of this, i.e. to carry out such mental operations, intelligence is required. In a word, artificial intelligence is an intelligent system capable of solving creative problems, traditionally considered the prerogative of humans.

A well-known example of improving artificial intelligence is computer games. Their first versions were simplified in their functionality, for example, playing chess or checkers, where the main actions are the player's piece move and reading the opponent's combinations. With the development of new technologies, machines began to acquire a completely different meaning and content. Now artificial intelligence is able to analyze the situation, calculate steps ahead and even beat people in computer games, which was previously unavailable.

There are practically no areas left where artificial intelligence has not yet found its application, due to which medicine, science, education, etc. are rapidly developing.

Let's take a closer look at the implications of machine learning for artificial intelligence.

Machine learning

Initially, the methods that were created for working with artificial intelligence were not suitable for solving complex issues. For example, rigid algorithms are not suitable for recognizing images or videos, text, or emotions. For this, machine learning came to the rescue - the field of artificial intelligence, which is responsible for the development of algorithms that can transform themselves without human assistance.

Simply put, these are methods that repeat the human learning system according to the principle “from simple to complex”. For example, like a schoolboy who learns to read: first, he learns the alphabet, then syllables, words, phrases, and finally - texts.

In approximately the same way, specialists develop Machine learning algorithms and provide them with a huge amount of data. Algorithms consider information and come to conclusions on the basis of which artificial intelligence is modernized. If the algorithm is provided with signs of cyber fraudsters attacking the banking platform, then the system, having learned from this example, will be able to calculate such actions on its own in the future.

It is important to understand that algorithms by themselves cannot analyze more accurate information, for this, there are neural networks, which we will talk about later.

Deep learning of neural networks

Artificial neural networks are mathematical models that replicate the structure of the human brain. They were created so that artificial intelligence can analyze images, text, human speech, etc.

Simple neural networks are capable of recognizing simple objects, distinguishing one from the other, or counting how many objects are depicted in a picture. More complex networks solve problems that computers could not cope with before.

For artificial intelligence to learn to distinguish between animals, it needs to provide them with labeled images. The probability of error-free identification becomes higher if you provide as many tagged images as possible.

But, as it turned out, this was not enough for the system to be able to analyze video material and recognize voices. For this, specialists began to work on deeper training of neural networks.

Deep learning

Deep learning of neural connections is one of the varieties of machine learning, a new stage in the development of science, where neural networks include various constituent elements that communicate with each other in extended boundaries. In this case, artificial intelligence can solve the most non-standard tasks.

The functionality of computer games, which we considered earlier, has become real thanks to Deep learning. Such deep neural networks can recognize complex images in real-time, for example, an airplane in a non-standard perspective, against any background, and even in a disguised form.

The data received by the system is analyzed by different layers of the neural network simultaneously. Each layer identifies the picture from its position.

There are three types of neural network layers :

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

Image recognition takes place in a hidden layer.

Deep learning plays a special role in speech analysis. A multilayer neural network is able to cope with a similar task: “France is my homeland, I lived in Peru and England. What language do I speak fluently? " The neural network will analyze this phrase, generate a list of languages ​​that the author probably knows, and eventually determine that the correct answer is French.

Deep learning became a reality after the development of productive computer systems, without which video recognition and analysis are impossible.

What tasks can machine and deep learning solve?

Both disciplines are designed to solve different types of problems. From the point of view of business processes, Machine learning is intended for:

  1. Business automation. Machine learning will be able to recognize users, analyze and organize customer data, and provide a personalized approach.
  2. Analyze data that needs to be structured and applied to train algorithms.

To apply Deep learning, the following conditions are required :

  1. A large array of information has not yet been analyzed and cannot be used to train algorithms.
  2. The need to solve problems that machine learning cannot handle.

From the foregoing, we can conclude that without artificial intelligence, machine, and deep learning, many computer functions would be inaccessible. For example, such as speech and image recognition and even checkers games. Due to the processing of a large amount of information and the identification of connections and patterns in it, machines are able to perform tasks of varying complexity.

The initial information always contains the answers required by professionals in different fields of activity. The main task is to learn how to find solutions using the latest technologies.

Today, the world is ruled by information and computer technology. And the winner is the one with the most advanced artificial intelligence.

We wish you good luck in using artificial intelligence in everyday life and do not forget about developing your own, because any machine, first of all, is created by us - people!


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