Here’s how it works: first, we feed the neural network some data and tell it what output we want from that data. For example, if our input is an image of a cat, we might want the output to be “cat” or maybe just a number representing how likely it is that the input is actually a cat (this is called a probability).

The neural network then goes through a series of steps to figure out what the best answer is. It starts by breaking down the input into smaller pieces, like pixels in an image or words in text. Then it uses these smaller pieces as “features” that can be combined and compared to other features to make predictions about the output.

For example, if we’re trying to identify whether a given image is of a cat or not, our neural network might look for certain patterns in the pixels (like furry shapes with eyes) and compare them to similar patterns it has seen before. If enough of these patterns match up, then the output will be “cat” or something close to that.

Now, here’s where things get a little bit more complicated: instead of just using fixed rules to make predictions (like “if there are furry shapes with eyes in an image, it must be a cat”), our neural network can learn from its mistakes and improve over time. This is called training the model, and it involves feeding it lots of different examples (both good and bad) until it gets really good at making accurate predictions.

To do this, we use something called “backpropagation” to calculate how much each individual neuron in our network contributed to a particular output. Then we adjust the weights between those neurons based on whether they were right or wrong (this is called updating the model). We repeat this process over and over again until the neural network can accurately predict the correct answer for most of the examples it’s been trained on.

It might sound complicated at first, but once you get used to the terminology (like “features” and “backpropagation”), it starts to make more sense. And who knows? Maybe someday we’ll all be using these things in our everyday lives without even realizing it!