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

The video tutorial explains the transition from using WX plus B as an output function to incorporating the sigmoid function for logistic regression. It details the definition of an output formula that takes features, weights, and bias as inputs. The implementation of the sigmoid function using the dot product and bias is demonstrated. The tutorial concludes with an overview of logistic regression and mentions upcoming topics.

Why is the sigmoid function used as an output function instead of WX + B?

• To simplify calculations

• To introduce non-linearity

• To increase the number of parameters

• To reduce computational cost

What is the role of the Numpy dot function in the output formula?

• It normalizes the input features

• It computes the dot product of vectors

• It calculates the sigmoid function

• It adds bias to the output

What is added to the dot product of X and weights in the output formula?

• A constant

• A bias

• A threshold

• A learning rate

Which of the following has been implemented along with the sigmoid function?

• Gradient Descent

• Linear Regression

• Cross Entropy

• Decision Trees

What is the next topic to be covered after the current lecture?

• Support Vector Machines

• Neural Networks

• K-Nearest Neighbors

• Logistic Regression