Mikkel

Change your weight slightly in this direction.

It's a bit like practicing a sport or an instrument.

You try, you get feedback, you adjust.

The difference is just that a neural network can do that thousands of times per second.

It's basically about having many layers of neurons stacked on top of each other.

More layers let the network learn more abstract features.

Early layers might learn simple things like edges or small patterns.

Deeper layers learn higher level concepts like shapes, objects, or even styles.

Human networks are powerful, but they can also overfit.

An overfit network might be perfect on the examples it saw, but terrible on new data.

The goal is always the same.

We don't want just a network that's good on yesterday's examples.

We want one they can handle tomorrow's.

Neural networks are the core engine behind computer vision models that work with images, language models like chatbots and translators, speech recognition, and recommendation systems, and many more.

Underneath, it's still layers of neurons, weights, activations, and learning from mistakes.

A neural network is a big function of tiny decisions.

You feed the numbers, they travel through layers of weighted connections, and you get an output.

It doesn't understand the world like we do, but it becomes very, very good at mapping input patterns to output patterns.

In the next episode of our How AI Works miniseries, we can go in a few directions.

Reinforcement learning, transformers, or a deeper but still simple explanation of how training at skill works.