There are many definitions for a Tensor. I will start from scratch: everybody should know what a number is, so lets take three numbers

a = 1
b = 2
c = 3

In tensor jargon they can be called real scalars and they have order zero. There is an elementary rule known as Distributive property

c * (a + b) = (c * a) + (c * b)

which is easy to check for scalars.

A real scalar can be represented geometrically by a point on a line, while if we need to identify a point in a plane we can use two coordinates. Such a point it is said to be represented by a real vector and they have order one. That means that the components of the vector are represented by one index varying from zero to the dimension of the space containing the vector. For example, given two vectors in a plane

a = [4, 5]
b = [-2, 1]

our dimension is two, and vector a can be represented as a_i where index i can be 0 or 1. For instance a_0 = 4 and a_1 = 5 are the two coordinates. Vectors inherits an addition operator naturally given by addition on each component. For example

a + b = [a_0 + b_0, a_1 + b_1] = [4 - 2, 5 + 1] = [2, 6]

Idem for subtraction. If we consider a scalar c = 2 we can check that Scalar multiplication preserves the Distributive property in fact

[4, 12] = 2 * [2, 6] = c * (a + b) = (c * a) + (c * b) = [8, 10] + [-4, 2] = [4, 12]

So far so good, this can be generalized also for higher dimensions. Now, I would introduce the concept of matrix, the reader may already know since they are used to describe geometric transformations, solve linear system, even to render the screen you are looking at now, it is used a matrix to represent the pixels. A matrix has two indices, one for rows and one for columns. For instance

M=(x11x12x21x22)M = \left( \begin{array}{ccc} x_{11} & x_{12} & \ldots \\ x_{21} & x_{22} & \ldots \\ \vdots & \vdots & \ldots \\ \end{array} \right)

The magic of tensors is that they generalize scalars, vectors and matrices and their operators, that is why I would like to build a matrix as the result of a Tensor product between two vectors. The product of two tensors a and b is a tensor t which order is the sum of orders of a and b and which components are given by multiply each component. For example, if we multiply our two vectors we obtain a matrix, which is a tensor of order two, which components are given by two indices

m = a x b = [4, 5] x [-2, 1] = [4 * (-2), 4 * 1, = [-8,  4,
                                5 * (-2), 5 * 1]    -10, 5]

where m_i_j = a_i * b_j, lets write it explicitly

m_0_0 = a_0 * b_0 = 4 * (-2) = -8
m_0_1 = a_0 * b_1 = 4 * 1 = 4
m_1_0 = a_1 * b_0 = 5 * (-2) = -10
m_1_1 = a_1 * b_1 = 5 * 1  = 5

It is so possible to build tensors of any order.

To complete the picture, there is another useful operator that is Tensor contraction. Given a tensor t of order N greater than one, with indices i_1, …, i_N, choosing two indices i_a and i_b, we can create another tensor of order N - 2 which components are given by summing over the indices choosed. For example, if we have a tensor t of order three, where t_i_j_k is a component and indexes vary from 0 to 2, choosing the first and the last index, the result after contraction is a tensor s which component s_j is given by

s_0 = sum t_i_0_k = t_0_0_0 + t_1_0_0 + … + t_2_0_2
s_1 = sum t_i_1_k = t_0_1_0 + t_1_1_0 + … + t_2_1_2
s_0 = sum t_i_2_k = t_0_2_0 + t_1_2_0 + … + t_2_2_2

It is so possible to many operators with Tensor product and Tensor contraction.