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Imagine that you want to learn how to translate text from English into Spanish. You learn languages by memorizing words and phrases, their meanings, and the context in which they are used. Based on this experience, you will be able to translate new texts that you have never seen before.

Another case is the classification of cats and dogs. Just as a person learns to distinguish them from examples seen in life, so a neural network can learn to distinguish them from such examples.

The neural network does something similar. It learns from examples - it can be texts, images, sounds, any data that we want it to process. A neural network, just like a person learns a language, tries to identify patterns in this data.

It then uses these patterns to perform tasks such as classification (determining which category an object belongs to), regression (predicting a numerical value such as the price of a house), or generation (creating new content based on the learned patterns). This process of training a neural network using examples is called supervised learning and this is the most common way to train it.

Note

When a neural network is trained, labeled examples are fed as input. When we want to get a prediction from it, inputs are not labeled.

This is a demonstration of a Neural Network specifically designed to identify drawings of cats and dogs.

It tackles a classification problem by processing an input from an initially unknown class and outputting the identified class.

Try using it to get a deeper understanding.

LMB (Left Mouse Button) - to draw.

Shift + LMB - to erase.

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.