Ayush

When you run data through the network, it flows one way or the other, from input through the hidden layers to the output.

This is called a forward path.

The magic of neural networks isn't just the forward paths.

It's how they learn from being wrong.

The adjustment step is done using an algorithm called Braque propagation, plus an optimizer like gradient descent.

You don't need to remember the names, just remember this idea.

If you repeat this over millions of examples, the network slowly replaces itself so that it guesses better and better.

You've probably heard the term deep learning.

So what makes a network deep?

A shallow network might have dozens of one hidden layer.

A deep network might have hundreds of layers.

That's why deep learning works so well for complex tasks like recognizing faces, translating languages, or playing strategy games.

It builds understanding in multiple stages.

Overfitting is when a model memorizes the training data instead of learning general patterns.

It's like a student who memorizes the answer key but doesn't actually understand the material.

To fight this, we use tricks, like training on more varied data, adding regularization, which gently limits how extreme the weights can get, using dropout, which randomly turns off some neurons during training, so the network doesn't rely too heavily on any one path.

We're trying to keep this series focused on how AI works, but not everything it's used for.

But it's still helpful to know how central neural networks are.

What changes between these applications isn't the basic neural network idea.

It's the input.