What are the applications of ReLU in neural networks?

What are the applications of ReLU in neural networks?

ReLU, or Rectified Linear Unit, is widely used in neural networks for its simplicity and efficiency. It's primarily used as an activation function in hidden layers.

Why is ReLU preferred over other activation functions?

ReLU helps mitigate the vanishing gradient problem, allowing models to learn faster and perform better, especially in deep networks.

Can you give examples of specific tasks where ReLU is beneficial?

Sure! ReLU is commonly used in image recognition, natural language processing, and reinforcement learning tasks.

Are there any drawbacks to using ReLU?

Yes, one drawback is the 'dying ReLU' problem, where neurons can become inactive and stop learning if they output zero for all inputs.

What are some alternatives to ReLU?

Alternatives include Leaky ReLU, Parametric ReLU, and ELU, each addressing some of ReLU's limitations.