Neural networks are a big part of how computers learn today it’s kind of like how our brain works but not exactly. They’re made up of layers of connected nodes or neurons where each node does some math and passes its result to the next one. When you give a neural network some data it moves through these layers and the network tries to figure out patterns in the data by adjusting the connections between the nodes.

For example if you want a neural network to recognize cats in pictures you feed it a bunch of images and over time it learns which features in the image are important for recognizing a cat. The more data it sees the better it gets at spotting cats but if the data isn’t good or it doesn’t see enough examples it might get confused and not work so well.

There are different types of neural networks some are simple with only a few layers and some are deep with many layers which is called deep learning. More layers mean the network can learn more complex things like understanding language or recognizing faces but it also means it takes more time and computing power to train.

Neural networks are used in all kinds of stuff today like voice recognition when you talk to Alexa or in image recognition like on Facebook when it tags your friends in photos. They’re really flexible and powerful but they don’t always get it right because they’re just looking for patterns and sometimes they pick the wrong patterns to focus on.

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