Mikkel

And I'm Mikkel.

In our last episode, we unpacked how AI deals with language in a simple way, ranking down natural language processing, or NLP, without the scary math.

If you've ever heard of deep learning and nodded like you understood but secretly didn't, this episode is for you.

We'll explain what a neural network is, how it learns, and why layers matter, all in plain language.

Let's start with a simple picture.

A neural network is a way for a computer to learn patterns from examples.

On its own, one neuron is not very smart.

But when you connect thousands or millions of them in layers, they can then do surprisingly complex things that recognize a face or answer a question.

A neuron takes a few inputs, which are just numbers, and combines them.

Each input has a strength attached to it called a weight.

Neuron multiplies each input by its weight, adds them up, and then applies a small formula called an activation function.

At a high level, a neuron is just take inputs, scale them, add them, pass through a squashing function, and then set the result onward.

The input layer is where you feed data in, numbers that represent an image, a sentence, or whatever you're working with.

Finally, there's an output layer, which might represent things like, is this a cat or a dog, or what word should come next?

At this point, the network is basically making its best guess based on its current settings, its current weights.

Here's the basic loop.

We show the network an example, say an image that is a cat.

The network makes a prediction.

This maybe says that it's a dog to measure how wrong it was using a loss, a number that represents the error, and the network adjusts its weights a tiny bit to reduce that error the next time.

Back propagation is like telling each neuron, here's how much you contributed to the mistake.