Python for Deep Learning - Build Neural Networks in Python - Feature Scaling

The video tutorial explains the importance of feature scaling in data processing, particularly in deep learning. It introduces the concept of standardizing independent features to a fixed range to improve calculation efficiency. The tutorial demonstrates how to implement feature scaling using the Standard Scaler class from the sklearn library in Python. It highlights the difference between scaling training and test data, emphasizing the use of fit_transform for training data and transform for test data. Finally, it verifies the scaled data using Jupyter Notebook.

Why is feature scaling important in deep learning?

• It eliminates the need for a test dataset.

• It increases the number of features.

• It ensures that all features contribute equally to the result.

• It reduces the size of the dataset.

What is the first step in implementing feature scaling using sklearn in Python?

• Transform the test data.

• Import the Standard Scaler class.

• Create a new dataset.

• Visualize the data.

When using the Standard Scaler, why do we apply 'fit_transform' to the training data but only 'transform' to the test data?

• To learn scaling parameters from the training data and apply them to the test data.

• To save time during computation.

• Because the test data is always smaller.

• Because the test data is already scaled.

What is the purpose of using 'fit_transform' on the training data?

• To create a new dataset.

• To learn the scaling parameters and scale the training data simultaneously.

• To visualize the data.

• To delete unnecessary features.

How can you confirm that feature scaling has been applied correctly in a Jupyter notebook?

• By printing the scaled data and observing the feasible range.

• By checking if the data is in a DataFrame format.

• By running a machine learning model.

• By comparing the original and scaled datasets.