Optimizing Gradient for Neural Network-based Project
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.C.
When working with a Neural Network-based project, it is common to encounter difficulties in optimizing the weights when the input data has features with different ranges. In this scenario, normalization is a common solution that can help improve gradient optimization and improve the model's performance.
Option B, which suggests using normalization, is the correct answer. Normalization is a technique used to adjust the values of the features to a common scale without distorting the differences in the range of values. It is usually performed by subtracting the mean of the feature from each value and dividing by the standard deviation.
Normalization helps in two ways:
- It brings all the features to a common scale, which can help the optimization algorithm converge faster, leading to better results.
- It helps to reduce the numerical instability in the optimization process, as it reduces the range of values that the optimization algorithm has to deal with.
Option A, which suggests using feature construction, can be useful in some scenarios. Feature construction involves creating new features from existing ones. However, it may not always be possible to combine features in a meaningful way, and it may not solve the problem of features with different ranges.
Option C, which suggests improving the data cleaning step by removing features with missing values, may help in improving the quality of the data. However, it does not address the issue of features with different ranges.
Option D, which suggests changing the partitioning step to reduce the dimension of the test set and have a larger training set, may help in improving the quality of the model by providing more training data. However, it does not address the issue of features with different ranges.
In conclusion, when faced with features with different ranges, it is best to use normalization to bring them to a common scale, which can help with gradient optimization and improve the performance of the Neural Network-based model.
