Includes bibliographical references and index.

Confusions: what are tensors exactly? -- Why and how are tensors used in machine learning? -- Direct sum space u ⊗ v -- Gibbs Dyadics -- Tensor spaces (tensor product u ⊗ v) -- Tensor spaces (tensor power V⊗(p,q)) -- Tensor algebra -- Dynamics: the inertia tensor -- Electrodynamics: the EM field tensor -- Riemannian geometry and general relativity -- Appendices: Topics of linear algebra -- Mathematical structures -- Axiomatic systems.

"Tensors have numerous applications in physics and engineering. There is often a fuzzy haze surrounding the concept of tensor that puzzles many students. The old-fashioned definition is difficult to understand because it is not rigorous; the modern definitions are difficult to understand because they are rigorous but at a cost of being more abstract and less intuitive. The goal of this book is to elucidate the concepts in an intuitive way but without loss of rigor, to help students gain deeper understanding. As a result, they will not need to recite those definitions in a parrot-like manner any more. This volume answers common questions and corrects many misconceptions about tensors. A large number of illuminating illustrations helps the reader to understand the concepts more easily. This unique reference text will benefit researchers, professionals, academics, graduate students and undergraduate students"-- Publisher's website.

Mode of access: World Wide Web.

System requirements: Adobe Acrobat reader.