Normalization in machine learning is a preprocessing technique used to scale numerical data to a common range without distorting differences in the ranges of values. This is done to ensure that no single feature dominates the model's learning process due to its scale.
How Normalization Works in Machine Learning
Normalization is often applied to datasets in machine learning before feeding them into a model. Common methods include scaling all values to a range between 0 and 1, or transforming the data so that it has a mean of 0 and a standard deviation of 1. Normalization helps speed up the learning process and can lead to better performance, especially in algorithms sensitive to the scale of input data, like neural networks.
Download this guide to delve into the most common LLM security risks and ways to mitigate them.
Lakera Guard protects your LLM applications from cybersecurity risks with a single line of code. Get started in minutes. Become stronger every day.
Several people are typing about AI/ML security. Come join us and 1000+ others in a chat that’s thoroughly SFW.