Deep Learning - Deep Neural Network for Beginners Using Python - Data Visualization and Reading

The video tutorial covers logistic regression, focusing on data visualization and code explanation. It introduces the dataset, explains the visualization process using libraries like Matplotlib, Numpy, and Pandas, and demonstrates how to read and split data into features and labels. The tutorial concludes with tasks for implementing logistic regression components, including the sigmoid function, prediction formula, and gradient descent.

What are the features and labels in the dataset used in the tutorial?

• Columns A and B are features, Column C is the label

• Columns A and C are features, Column B is the label

• All columns are features

• Columns B and C are features, Column A is the label

How is the data split into categories for visualization?

• Based on the value of the label

• Based on the number of features

• Based on the number of rows

• Randomly

What color represents admitted individuals in the visualization?

• Blue

• Green

• Red

• Yellow

Which library is used to read the CSV data file?

• Pandas

• Scikit-learn

• Numpy

• Matplotlib

What is the purpose of the sigmoid function in logistic regression?

• To split the data

• To predict probabilities

• To update the weights

• To calculate the error

What is the role of the learning rate in updating weights?

• It sets the initial weights

• It decides the number of iterations

• It controls the step size during optimization

• It determines the number of features

Which formula is used to calculate the error in logistic regression?

• Mean Squared Error

• Cross Entropy

• Absolute Error

• Hinge Loss