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.

Describe the vanishing gradient problem in the context of neural networks.

How does the sigmoid function transform real number inputs?