Can you explain what Leaky ReLU is?
Leaky ReLU is an activation function used in neural networks that allows a small, non-zero gradient when the input is negative.
How does it differ from the standard ReLU?
Unlike standard ReLU, which outputs zero for negative inputs, Leaky ReLU outputs a small slope, preventing dead neurons.
What are the advantages of using Leaky ReLU?
Leaky ReLU helps mitigate the vanishing gradient problem and allows for better training of deep networks.
Are there any downsides to using it?
One downside is that it can introduce noise in the model, and the choice of the slope can be somewhat arbitrary.
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