Python for Deep Learning - Build Neural Networks in Python - Rectified Linear Unit (ReLU) Function

The video tutorial explains the Rectified Linear Unit (ReLU) function, a key activation function in neural networks. It describes how ReLU outputs zero for negative inputs and the input value for non-negative inputs. The function is widely used in convolutional neural networks and deep learning due to its ability to prevent vanishing gradient problems. ReLU is differentiable but not monotonic, with a range from zero to infinity. However, it has limitations, such as data loss for negative inputs, which can lead to instability in neural networks. The tutorial concludes with a brief summary and a transition to the next lecture.

Explain why the rectified linear unit function is not a monotonic function.

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What is the range of the rectified linear unit function?

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