What is backpropagation in neural networks?

Can you explain what backpropagation is in neural networks?

Backpropagation is a training algorithm used in neural networks to minimize the error by adjusting weights.

How does backpropagation actually work?

It works by calculating the gradient of the loss function with respect to each weight by the chain rule.

Why is backpropagation important for training neural networks?

It's crucial because it allows the model to learn from errors and improve its predictions over time.

Are there any alternatives to backpropagation?

Yes, alternatives include genetic algorithms and reinforcement learning methods, but backpropagation is the most common.