Neural Network Structure

Neural Network Structure

A neural network is a structure made up of layers of "neurons", similar to biological neurons in the brain. Each neuron processes information, receives input, and passes the result on to the next layer. The image below illustrates a simple artificial neural network (ANN) with three layers: input, hidden, and output.

• The input layer receives data;
• The hidden layer processes information through weighted connections;
• The output layer produces the final result.

Like learning a language, the network refines its understanding through repeated exposure to data, recognizing patterns, and improving predictions.

Neurons in a neural network are connected by weighted connections, where each weight represents the importance of the link between two neurons. As shown in the image, each neuron in one layer is connected to every neuron in the next layer, allowing information to flow through the network.

Note

The thicker the connection, the more important it is.

The process of training a neural network is to adjust the "weights" of each neuron in such a way that the results they give are the most accurate. It's a bit like how we learn to play a musical instrument, gradually improving our skills and accuracy.

However, it is important to understand that neural networks are only a tool, they do not have their own consciousness or understanding of the world, like a person. They simply process the data and find the patterns that we asked them to find. And a neural network trained to predict the price of a house would not be able to predict the price of a guitar in a music store.