Many deep-learning systems available today are based on tensor algebra, but tensor algebra isn’t tied to deep-learning. It isn’t hard to get started with tensor abuse but can be hard to stop. Ted Dunning, Chief Applications Architect at MapR Technologies. Tensors and new machine learning tools such as TensorFlow are hot topics these days, especially among people looking for ways to dive into deep learning. Turns out, when you look past all the buzz, there’s really some fundamentally powerful, useful and usable methods that take advantage of what tensors have to offer, and not just for deep learning situations. Here’s why. If computing can be said to have traditions, then numerical computing using linear algebra is one of the most venerable. Packages like LINPACK and the later LAPACK, are now very old, but are still going strong. At its core, linear algebra consists of fairly simple and very regular operations involving repeated multiplication and addition operations on one- and two-dimensional arrays of numbers (often called vectors and matrices in this context) and it is tremendously general in the sense that many problems can be solved or approximated by linear methods. These range from rendering the images in computer games to nuclear weapon design as well for as a huge range of other applications between these extremes.