Singular Value Decomposition Algorithms Singular Value Decomposition concepts Singular Value Decomposition (SVD) is a matrix factorization technique used in various machine learning and data analysis applications. It decomposes a matrix into three separate matrices that capture the underlying structure of the original matrix. The three matrices that SVD produces are:   U: a unitary matrix that represents the left singular vectors of the original matrix. S: a diagonal matrix that represents the singular values of the original matrix. V: a unitary matrix that represents the right singular vectors of the original matrix. Here is an example of how SVD works : Suppose we have a matrix that represents the ratings of users for different movies. We can use SVD to decompose this matrix into three separate matrices: one matrix that represents the preferences of users, one matrix that represents the importance of each movie, and one matrix that captures the relationship between users and m