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What are neural networks

Neural networks are small models consisting of small processing units that you will find in the human brain (brain cells called “neurons”) Scientists make these models and make them behave wisely. They can be taught to do things that traditional computers do difficult. Early neural networks were called multilayer perceptrons.

Another name for neural networks is “connectionist networks,” and the people who make and develop them are often called “connectionists” networks, often taking the form of computer programs. These programs mimic these models, although more and more silicon chips are produced using neural networks designed directly in the form of electronic components.

The main feature of the network is that it learns from experience. If you want to distinguish male faces from female faces, you should show a large number (the more, the better) photographs of faces, and tell the network which of them are male and which are female. The network is adapted to recognize the differences between male and female faces.

The structure of neurons in the brain was first explained by McCulloch and Pitts in 1940. Neural networks seemed to be very promising in the early days of computing, but they fell out of favor, and only recently they returned. Nowadays, they are starting to appear everywhere, from training some little robot to navigate the circular maze, to acting as an artificial “nose” that can smell the difference between fine wine and cheap wine. How are neural networks really accurate?

Neural networks are typically used as classifiers. This means that they are used to choose between several options when faced with some input. For example, spoken words can be provided as input and you need to find out what the words are (that is, choosing one word from the possible dictionary).

Of course, they can do other things. Neural networks can act as a processor of address directories. This means that they can store complex samples, and then you can offer them the part of the sample or version of the sample that has been damaged, and they will display all the original undamaged samples. The neural network receives a quarter of the sample, and it reproduces the picture as a whole from this quarter.

Of course, once you start using different neural networks together, you can get much more complex behavior. After all, the human brain is a huge number of specialized neural networks – everything works together to give complex behavior. Such systems are commonly referred to as multi-network architecture.

Networks are made up of small elements called cells, and they are connected to each other in such a way that they can transmit signals to each other. In practice, the signal that one cell sends another prime number from 0 and 1, 0 means that there is no signal, 1 means a large signal. Compounds have certain strengths or weights. The network begins by setting these forces in an arbitrary way. The network is exposed to various inputs, and the forces are adjusted according to some mathematical rules. This is what we call learning, and after that the network can recognize the input signals or at least do something sensible. Information is stored in the forces of communication, just as it happens in the human brain. Learning is done in tiny steps. Networking takes a lot of time.

Neural networks are small models consisting of small processing units that you will find in the human brain (brain cells called “neurons”) Scientists make these models and make them behave wisely. They can be taught to do things that traditional computers do difficult. Early neural networks were called multilayer perceptrons.

Another name for neural networks is “connectionist networks,” and the people who make and develop them are often called “connectionists” networks, often taking the form of computer programs. These programs mimic these models, although more and more silicon chips are produced using neural networks designed directly in the form of electronic components.

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