How can neural networks be fine-tuned?

Neural networks can be fine-tuned by making small changes to help them work better for a specific job.

Think of a neural network like a robot that learns how to draw pictures. At first, it might draw messy shapes, but if you give it more practice with the right kind of pictures, say, cats, it gets better at drawing cats.

Fine-tuning is like giving the robot extra help after it has already learned some basic skills. It's like when your teacher gives you a special homework to get really good at one thing, maybe adding extra practice questions about addition, so you can master that before moving on.

Imagine your robot is learning to draw, and now it’s focusing just on dogs. You give it lots of pictures of dogs to look at, this is like giving it a new kind of training. The robot adjusts its drawing technique based on these new examples, making the drawings of dogs clearer and more accurate.

It's like when you practice writing your name every day, over time, you get faster and neater. That’s fine-tuning in action!