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

The video tutorial introduces the sigmoid function, providing its definition and formula. It then discusses the implementation approach, highlighting a conventional change from using Z to X as a variable. The tutorial proceeds to define the sigmoid function in code, explaining the return statement that uses the numpy exponential function.

What is the mathematical expression for the sigmoid function?

• 1 / (1 - e^(-Z))

• 1 / (1 + Z)

• 1 / (1 + e^(-Z))

• 1 / (1 + e^(Z))

Why is the variable Z replaced with X in the implementation?

• Because X is easier to compute

• Because X is a constant

• Because Z is not a valid variable

• Because X is a feature vector

In the context of the sigmoid function, what does the term 'feature vector' refer to?

• A constant value

• A mathematical constant

• A set of input features

• A single numerical value

Which library is used to compute the exponential part of the sigmoid function in Python?

• math

• scipy

• numpy

• pandas

What does the sigmoid function return when implemented in Python?

• 1 / (1 + numpy.exp(X))

• 1 / (1 + numpy.dot(X))

• 1 / (1 + numpy.exp(-X))

• 1 / (1 + numpy.log(-X))