What should you do?

You are working on a Neural Network-based project. The dataset provided to you has columns with different ranges. While preparing the data for model training, you discover that gradient optimization is having difficulty moving weights to a good solution.

What should you do?
A . Use feature construction to combine the strongest features.
B . Use the representation transformation (normalization) technique.
C . Improve the data cleaning step by removing features with missing values.
D . Change the partitioning step to reduce the dimension of the test set and have a larger training set.

View Answer

Answer: B

Explanation:

Representation transformation (normalization) is a technique that transforms the features to be on a similar scale, such as between 0 and 1, or with mean 0 and standard deviation 1. This technique can improve the performance and training stability of the neural network model, as it can prevent the gradient optimization from being dominated by features with larger scales, and help the model converge faster and better. There are different types of normalization techniques, such as min-max scaling, z-score scaling, log scaling, etc.

You can learn more about normalization techniques from the following web search results:

Normalization | Machine Learning | Google for Developers

NORMALIZATION TECHNIQUES IN TRAINING DNNS: METHODOLOGY, ANALYSIS AND …

Visualizing Different Normalization Techniques | by Dibya … – Medium Data Normalization Techniques: Easy to Advanced (& the Best)