Python for Deep Learning - Build Neural Networks in Python - Leaky Rectified Linear Unit function

The video tutorial introduces the leaky version of the rectified linear unit (ReLU) function, highlighting its modification to address the negative impact of the standard ReLU. It explains the typical value for the parameter 'a' in the function's diagram and discusses the range of the leaky ReLU, which extends from negative to positive infinity. The tutorial also covers the differentiable and monotonic nature of the leaky ReLU function.

What is the purpose of the leaky version of the rectified linear unit function?

How does the leaky Relu function address the negative impact of the standard rectified linear unit function?

What is the typical value for 'a' in the leaky Relu function diagram?

What is the range of the leaky Relu function?

What characteristics make the leaky Relu function differentiable and monotonic?