Tensors

While you may have heard of vectors and matrices before, the name tensor may be new. A tensor is a generalized matrix, and they have different sizes, or ranks, which measure their dimensions.

Tensors are three (or more)-dimensional lists; you can think of them as a sort of multi-dimensional object of numbers, such as a cube. Tensors have a unique transitive property and form; if a tensor transforms another entity, it too must transform. Any rank 2 tensor can be represented as a matrix, but not all matrices are automatically rank 2 tensors. A tensor must have this transitive property. As we'll see, this will come into play with neural networks in the next chapter. We can create tensors in Python such as the following: 

tensor = [[[1,2,3,4]],[[2,5,6,3]],[[7,6,3,4]]] 

Within the context of AI, tensors can represent things such as word embeddings or weights in a neural network. We'll talk about these more as we encounter at them in upcoming chapters.