Deep Learning - Deep Neural Network for Beginners Using Python - Sigmoid Function

The video tutorial explains the transition from using a step function to a sigmoid function in machine learning models. Initially, the step function, which outputs discrete values of 0 or 1, is discussed. The limitations of this approach are highlighted, leading to the introduction of the sigmoid function. The sigmoid function normalizes values between 0 and 1, providing continuous outputs. The tutorial further explains the mathematical formula of the sigmoid function and its implementation in models, replacing the step function to achieve continuous value outputs.

Describe the process of how the sigmoid function is implemented in a neural network model.

What are the advantages of using continuous values over discrete values in neural networks?