Is L2-Norm = Euclidean Distance?

One of the concepts that can be a little confusing is the difference between Norms and Distances in Machine Learning. When do you call it an L2 Norm or euclidean distance? Today let's clarify this forever. Let's say we have a 2D vector A. The distance of vector A from the origin is called the norm of the vector A. As you can see, this is how we represent a vector in 2D and the distance from the origin to vector A is called the Norm of Vector A. This distance can be calculated using various methods such as Euclidean distance, Manhattan distance, etc. Let's calculate the distance of Vector A from the origin using Euclidean distance, this is how it will look like for 2D. Vector Norm using Euclidean distance is also called L2-Norm.