What is gradient descent and how does the sigmoid function relate to it?

What is gradient descent and how does it work?

Gradient descent is an optimization algorithm used to minimize a function by iteratively moving towards the steepest descent, or the negative gradient.

Can you explain how the sigmoid function fits into this?

The sigmoid function is often used in logistic regression and neural networks. It maps any real-valued number into a value between 0 and 1, which is useful for binary classification.

Why is the sigmoid function preferred in some cases?

The sigmoid function provides a smooth gradient, which helps in optimizing the weights during training, making it easier for gradient descent to converge.

Are there any drawbacks to using the sigmoid function?

Yes, one drawback is the vanishing gradient problem, where gradients become very small, slowing down the learning process.