sing ReLu activation function is a solution for the issues of _________.1 pointvanishing gradientsexploding gradientslong-term independencies of gradientsexponential gradients

Using ReLu (Rectified Linear Unit) activation function is a solution for the issues of vanishing gradients.

Here's why:

1. The ReLu function is defined as f(x) = max(0, x). This means that for all positive inputs, the output is the same as the input, and for all negative inputs, the output is 0.

2. Because of this, the gradient or derivative of the ReLu function is 1 for all positive inputs and 0 for all negative inputs.

3. This means that during backpropagation, when the gradients of the loss function with respect to the weights are being calculated, the gradients do not get multiplied by small numbers (as is the case with other activation functions like sigmoid or tanh).

4. As a result, the issue of vanishing gradients, where the gradients become so small that the weights of the neural network do not get updated significantly, is mitigated.

5. However, it's important to note that while ReLu helps with the vanishing gradients problem, it can cause a different issue known as the "dying ReLu" problem, where neurons can become stuck on zero and no longer update. There are variations of ReLu such as Leaky ReLu and Parametric ReLu to help mitigate this issue.