How Does Vanishing AND Exploding Gradient Problem Explained | Deep Learning 6 Work?

A neural network is like a team of whisperers passing messages from one end to the other, but sometimes those messages get too quiet or too loud, making it hard for everyone to understand.

Imagine you're in a long line with your friends, and each person whispers a message to the next. If the message gets too quiet, by the time it reaches the end of the line, it's barely audible, that’s like the vanishing gradient problem. The people at the end can’t hear what was said clearly.

On the flip side, if the message gets too loud, it becomes a roar, and everyone gets confused. That’s the exploding gradient problem. It’s like your friend shouts so loudly that everyone in the line starts shouting too, making things messy.

In a neural network, each layer of whisperers (or neurons) tries to learn from the messages it receives. But if the message gets too quiet or too loud, the learning process breaks down, just like how your friends might forget what they were supposed to say if the message is unclear.

It's like trying to build a tower with blocks, but some of them are so light you can barely see them, and others are so heavy they almost knock you over. That’s not fun for anyone!