Deep Learning - Deep Neural Network for Beginners Using Python - Scaling the Data (NN Implementation)

The video tutorial covers storing data into a processed data variable, scaling GPA and GRE columns by dividing them by their maximum values, and explaining the scaling process. It also demonstrates how to preview the scaled data and concludes with splitting the data into training and testing sets, as well as separating features and labels.

What is the purpose of storing data into a processed data variable?

• To delete unnecessary data

• To encrypt data

• To prepare data for further operations

• To visualize data

What happens when you divide a column by its maximum value?

• The column values become negative

• The column values remain unchanged

• The column values are multiplied by the maximum value

• The maximum value becomes one

What was the maximum value used for scaling the GPA column?

• 50

• 100

• 10

• 40

What is the next step after scaling the data?

• Visualizing the data

• Splitting the data into training and testing sets

• Encrypting the data

• Deleting the data

What is the purpose of splitting data into features and labels?

• To improve data visualization

• To reduce data size

• To prepare for model training

• To enhance data security