New technologies that appear are connected precisely with artificial intelligence. Also, there is such a thing as deep intelligence, which has also become the basis for many developments. And today we will talk about it in more detail.
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The first type of training is now being used more actively, but it is limited in opportunities. In short, in order to analyze any data in Machine learning, it is necessary to set a template or classify information.
But deep learning algorithms do not require any source code, since they work on the principle of neural connections, performing much more operations to search for all information without a given classification.
For the first time, deep learning in machine learning was talked about a little over 20 years ago, but previously there was no technical infrastructure for deep learning of AI. The power of computers was simply not enough to process a large amount of data comparable to the human brain. But now, with the advent of high-performance video processors, this has become possible.
For a more accurate understanding of deep learning technology in AI, one should learn to distinguish between concepts that are often confused or compared with each other. Artificial intelligence is a concept that includes two elements at once, namely deep learning, and of course machine learning. Both approaches to the search and processing of information by computers relate specifically to AI, but differ in the implementation of the solution.
For instance, to teach a computer to identify faces from CCTV cameras in an office using machine learning, you first need to upload photographs of employees, and assign a name or number to each of them using a photo. Naturally, even the smartest machine learning robot itself will not be able to identify people from a photo or video that it receives from a camera. First you need to provide the initial data, and in full and with absolute accuracy. Only then can the robot recognize the models.
And only the one that it was taught will do the work of the computer in machine learning. Without the ability to expand your knowledge and skills exponentially.
How does deep learning work then?
Computers for deep learning are able to expand knowledge through neural networks. The human brain works on the same principle, but only on a large scale. For example, if, during deep learning, a computer is simply provided with photographs of animals, it will, over time, determine their breed and characteristics. In the case of machine learning, the computer would first have to provide a database with a list of animal species and their photos for comparison and identification.
In deep learning models are analyzed on the basis of data that is available on the Internet or in a specialized database.
For example, in deep learning, data processing is carried out in stages. That is, first, the neural network reacts to basic factors: changes in colors, then the computer determines the geometric shapes and understands that there is an image in front of it. Then the precise outlines are recognized and so on.
In fact, deep learning examples are more extensive than just identifying animals from photographs, but more on that later.
