Deep Learning 101: Demystifying Tensors

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. 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. The absolutely fundamental operation of linear algebra as implemented on computers is the dot product of two vectors.