This book covers all about tensor factorization in a generalized way. Generalization is accomplished by use of various divergence functions as well as different hidden structures. The divergence functions are generalized by the beta divergences that are connected to the Tweedie Models. The hidden structure is generalized by use of invented abstract factorization notation. Various learning algorithms including coupled tensors are, then, derived accordingly.