You can compare a neural network to a chess game with a computer. It has algorithms, according to which it determines tactics, depending on your moves and actions. The programmer enters data on how each figure moves into the computer’s database, determines the boundaries of the chessboard, introduces a huge number of strategies that chess players play by. At the same time, the computer may, for example, be able to learn from you and other people, and it can become a deep neural network. In a while, playing with different players, it can become invincible.
The neural network is not a creative system, but a deep neural network is much more complicated than the first one. It can recognize voice commands, recognize sound and graphics, do an expert review, and perform a lot of other actions that require prediction, creative thinking, and analytics. Only the human brain has such possibilities. The neural network can get one result (a word, an action, a number, or a solution), while the deep neural network solves the problem more globally and can draw conclusions or predictions depending on the information supplied and the desired result. The neural network requires a specific input of data and algorithms of solutions, and the deep neural network can solve a problem without a significant amount of marked data.