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.

What happens to the ReLU function when the input value is less than zero?

• The function outputs one.

• The function outputs the input value.

• The function outputs infinity.

• The function outputs zero.

Why is the ReLU function commonly used in deep learning?

• It is computationally expensive.

• It causes vanishing gradient problems.

• It helps prevent vanishing gradient problems.

• It is not differentiable.

Which of the following is true about the ReLU function?

• It is a monotonic function.

• It has a range from negative infinity to infinity.

• It is not a monotonic function.

• It is not a differentiable function.

What is a major drawback of the ReLU function?

• It enhances data for values less than zero.

• It stabilizes the neural network.

• It loses data for values less than zero.

• It maps all values to one.

What is the range of the ReLU function?

• From negative infinity to infinity.

• From zero to infinity.

• From negative infinity to zero.

• From zero to one.