Pros:
Filtering out excess noise coming in with the input data. It's like at a stadium: if you're talking to one person, you hear not only their voice, but also a lot of extraneous sounds (music, singing, fans' shouts, etc.). But your brain filters all of this out and clearly perceives only the speech of the interlocutor. Neural networks can do this too. They are trainable, can remember and filter incoming information, rejecting excess unnecessary noise.
Self-learning ability. A saudi arabia email list simple example: you constantly use the VKontakte app and have already gotten used to all its functions. And suddenly it is updated and additional features appear (TikTok videos, damn them). It takes you literally a few minutes to figure out how everything works here so that you can continue using the app without any problems. Well, this is not a problem for ANNs either. Networks are able to study changes, adapt to them and then continue to function normally.
Maintaining functionality when some network elements fail. Here, again, everything is similar to the work of a living brain. It happens that a person has part of the brain removed (due to illness) and then the healthy sections begin to perform the work of the removed diseased part. The result is that the person calmly continues to live on, retaining all of his vital functions. And what is a neural network? It is an artificially created prototype of the brain. Therefore, here too, if some neurons fail, the system still continues to give generally logical answers.
Speed of operations. One neuron is like an independent microprocessor. And in the entire network there are thousands of such neurons, and they are all interconnected. Incoming tasks are distributed and solved in them very quickly. Conventional algorithms function much more slowly.
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Alexander Kuleshov
Alexander Kuleshov
General Director of Sales Generator LLC
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Cons:
Inaccuracy of the answer. It always turns out to be approximate. Sometimes even the given answer does not match the one that will be considered incorrect by only a few percent, but there is no way to change the situation.
Complex multi-stage solutions. In simple terms, ANN does not solve the problem sequentially, moving from one point to another. Here, each neuron is a separate microprocessor, acting at its own discretion, and it is not interested in how its neighbors work.
Inability to perform computational processes. The two previous shortcomings are the reason for this. After all, even the simplest equation is solved by sequentially performing a number of steps, and this is precisely what neural networks cannot do.