Deep Learning - Crash Course 2023 - Data Standardization - 1

The video tutorial covers the UCI heart disease dataset, explaining how to import data using pandas and numpy. It introduces the concept of a sigmoid neuron, detailing its function and application. The tutorial emphasizes the importance of data standardization, explaining how to use the standard scalar from scikit-learn to preprocess data. A practical demonstration of applying the standard scalar to random data is provided, highlighting the transformation process and its effects on data distribution.

What is the first step in preparing the UCI heart disease dataset?

• Importing necessary libraries

• Applying data standardization

• Visualizing the data

• Training the model

What is the purpose of separating input and output data in the dataset?

• To prepare for model training

• To reduce data size

• To visualize the data

• To apply different algorithms

Which function is used in a sigmoid neuron?

• 1 / (1 + e^(-z))

• 1 / (1 + e^(z))

• e^(-z) / (1 + e^(-z))

• z / (1 + e^(-z))

What is a challenge when dealing with features of varying magnitudes in a dataset?

• Features with larger magnitudes dominate

• Features with smaller magnitudes dominate

• Data becomes more complex

• All features contribute equally

What is the purpose of data standardization?

• To increase data size

• To ensure features have equal contribution

• To simplify data visualization

• To remove outliers

What are the two properties of data after standardization?

• Mean equal to 1 and standard deviation equal to 0

• Mean equal to 0 and standard deviation equal to 0

• Mean equal to 1 and standard deviation equal to 1

• Mean equal to 0 and standard deviation equal to 1

What is the first step in applying standard scaling to data?

• Transforming the data

• Visualizing the data

• Creating an instance of StandardScaler

• Calculating the mean

What method is used to compute the mean and standard deviation for scaling?

• transform()

• fit()

• scale()

• apply()

What is the benefit of using the fit_transform method?

• It visualizes the data

• It increases data complexity

• It combines fitting and transforming in one step

• It reduces data size

What is the result of applying standard scaling to data?

• Data is visualized

• Data size is reduced

• Data has a mean of zero and a standard deviation of one

• Data becomes more complex