Vector Normalization L1 & L2 #shorts #ai #ml

Welcome to this 20-second YouTube short on Vector Normalization L1 & L2! In machine learning and artificial intelligence, vector normalization is a technique used to scale vectors to a common length. L1 and L2 are two common normalization methods that are widely used in data preprocessing. L1 normalization scales the vector based on the sum of absolute values of its components, while L2 normalization scales it based on the square root of the sum of squared values. Both methods have their advantages and are used in different contexts. By understanding vector normalization L1 & L2, you can improve your understanding of data preprocessing in AI and ML. So join us for a quick explanation of this important technique, and discover how you can apply it to your own projects.