How does a neural network work?

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The neuron structure is recreated using software code, using a cell instead of an axon, which contains a limited range of values. Information about the "nerve impulses" is represented by mathematical formulas and numbers.

The connections between neurons are also implemented in software. One neuron passes the calculated information to another neuron, which then receives, processes this information and passes the result on. This process allows information to spread throughout the network, and the internal parameters of neurons change during the malaysia mobile phone numbers database learning process.

How does a neural network work?

The principles of operation of a neural network can be divided into several stages:

Forward pass . At this stage, the input data passes through layers of neurons, where mathematical processing of the data and activation of neurons occurs. The process continues until reaching the output layer.

Error calculation . After the data has passed through the neural network, its output results are compared with the expected ones. The difference between the obtained and expected results is calculated as the error.

Backpropagation . Using information about the error, the neural network begins backpropagation of the error. This process allows it to determine which neurons and layers of the network were most responsible for the error.

Weight update . At this stage, the network adjusts the weights within each neuron. This process is carried out based on the information about the errors that were calculated at the previous stage. Weight adjustment is aimed at reducing the number of errors and improving the accuracy of the model's prediction.

Let's look at an example. We have a neural network that is being trained to work on recognizing images of cats and dogs. In the forward pass stage, the image is fed to the input layer of the neural network, and then the data passes through hidden layers, where the mathematical processing of the information is carried out.

Then, at the output layer of the neural network, neurons are activated and the result is output: cat or dog. If the result does not match the expected one (for example, there was a dog in the image, but the neural network identified a cat), then the backpropagation stage of the error occurs.

At this stage, the weights of the neurons are adjusted to reduce the error and improve the accuracy of the assumptions. This allows the neural network to gradually adjust and become more accurate in recognizing images of cats and dogs.