I am new to machine learning, and I am taking an online class to develop an AI. I would like to understand how it all works, rather than just copying the code so I can implement my own AIs in the future, but my first question is what exactly is a tensor? From what I can find online, it is basically a multi-dimensional array.
For instance, a tensor with size [2, 2, 2] means that there is there is a 3-d array with size 2x2x2. If I were to extend that to [2, 2, 2, 2], each dimension of the 3-d object would have an additional dimension. Is that correct? So a [2, 2, 2, 2] array would hold 16 objects (2x2x2x2 = 16)?
If that is the case, what is the benefit of using a tensor object versus simply a numpy array? Can a tensor hold additional data or are there things that can be done on a tensor that cannot be done in a numpy array?
Thanks to all who aid me in the beginning of my adventure into machine learning!