What problem can be mitigated by using leaky ReLU as an activation function?

Leaky ReLU is an activation function that addresses the issue of vanishing gradients. This problem typically arises in deep neural networks when activation functions, such as traditional ReLU or sigmoid, cause gradients to become very small during backpropagation. As the network becomes deeper, the gradients can diminish to the point where they are ineffective for updating the weights, ultimately hindering the training process.

Leaky ReLU helps to prevent this by allowing a small, non-zero gradient when the input is negative. This means that even when neurons become inactive (outputting a zero with traditional ReLU), there is still a slope through which gradients can propagate. This maintains a flow of information and helps in updating weights, thereby mitigating the vanishing gradient problem and enabling deeper networks to learn more effectively.

In contrast, overfitting refers to a model that learns the training data too well, capturing noise along with underlying patterns, which is not directly influenced by the choice of activation function. Underfitting occurs when a model is too simple to learn the underlying structure of the data, which is also unrelated to the activation function choice. Data imbalance involves a disproportionate representation of classes within a dataset, and while it impacts the overall model performance, it is not resolved by changing