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.

What is the primary characteristic of the step function as an activation function?

• It outputs discrete values of either 0 or 1.

• It is used for non-linear transformations.

• It normalizes values between 0 and 1.

• It outputs continuous values.

How does the sigmoid function differ from the step function?

• It outputs discrete values.

• It normalizes values between -1 and 1.

• It is only used for binary classification.

• It provides continuous output between 0 and 1.

What happens to the sigmoid function's output when the input value is zero?

• The output is exactly 1.

• The output is exactly 0.

• The output is approximately 0.5.

• The output is undefined.

Why is the sigmoid function preferred over the step function in some models?

• It is faster to compute.

• It is easier to implement.

• It provides continuous output, which is often more useful.

• It requires less data to train.

What is the formula for the sigmoid function?

• Z / (1 + e^-Z)

• Z / (1 + e^Z)

• 1 / (1 + e^-Z)

• 1 / (1 + e^Z)