Python for Deep Learning - Build Neural Networks in Python - The Sigmoid Function

The video tutorial covers the sigmoid function, highlighting its range between 0 and 1, differentiability, and monotonic nature. It discusses its use in probability models and its limitations in real-world applications due to computational inefficiency, non-zero centering, and the vanishing gradient problem. The vanishing gradient issue is explained in the context of neural networks and backpropagation. The tutorial concludes by summarizing the sigmoid function's ability to squeeze any real number into a value between 0 and 1.

What is the range of values that the sigmoid function can output?

• Between -1 and 1

• Between 0 and 2

• Between 0 and 1

• Between -0.5 and 0.5

Why is the sigmoid function considered differentiable?

• Because we can find the slope at any two points

• Because it is a linear function

• Because it is not continuous

• Because it has a constant slope

Which of the following is a limitation of the sigmoid function?

• It is computationally expensive

• It is zero-centered

• It is not differentiable

• It causes vanishing gradient problems

What problem does the sigmoid function face when used in neural networks?

• Exploding gradient problem

• Vanishing gradient problem

• Overfitting problem

• Underfitting problem

What does the sigmoid function do to any real number input?

• Converts it to a value between -1 and 1

• Converts it to a value between 0 and 1

• Converts it to a value between 0 and 2

• Converts it to a value between -0.5 and 0.5