Yes, the max(0, x) function is a rectified linear unit (ReLU) activation function and the max(0.1x, x) function is a leaky ReLU activation function. Both of these functions are commonly used in deep learning as activation functions for neural networks.
The ReLU function takes the maximum of 0 and the input value, x. This means that for any input x less than 0, the output of the ReLU function is 0, and for any input x greater than or equal to 0, the output is x. The ReLU function has the property of being non-linear, which is important for deep learning models because it allows the model to learn complex relationships between the input and output.
The leaky ReLU function is similar to the ReLU function, but it allows a small negative slope for input values less than 0. This is done by replacing the 0 in the ReLU function with a small negative value (e.g. 0.1x). The leaky ReLU function is often used to alleviate the "dying ReLU" problem, which occurs when the ReLU function outputs 0 for a large number of input values, resulting in the model being unable to learn.
Both the ReLU and leaky ReLU functions are commonly used as activation functions in deep learning models because they are simple and efficient to compute, and they have been shown to work well in a variety of tasks.
