Update README.md

README.md

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 [![Codecov](https://codecov.io/gh/lssc-team/TensorValues.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/lssc-team/TensorValues.jl)
+
+If you ❤️ this project, give us a ⭐️!
+
+**TensorValues** provides the types `VectorValue` (a 1-st order tensor), `TensorValue` (a 2-nd order tensor) and `MultiValue` (a generalization of `VectorValue` and `TensorValue`) and common tensor operations defined on these types (e.g., dot product, inner produc, outer product, etc.)
+
+## Why
+
+The main feature of the **TensorValues** package is that the provided types `VectorValue`, `TensorValue`, and `MultiValue` do not extend from `AbstractArray`, but from `Number`!
+
+This allows one to work with them as if they were scalar values, which is specially usefull, when using broadcasted operations on arrays of `VectorValue` or `TensorValue` objects. For instance, one can perform the folowing manipulations:
+```julia
+# Assing a VectorValue to all the entries of an Array of VectorValues
+A = zeros(VectorValue{2,Int}, (4,5))
+v = VectorValue(12,31)
+A .= v # This is posible since  VectorValue <: Number
+
+# Broatcasing of tensor operations in arrays of TensorValues
+t = TensorValue(13,41,53,17) # creates a 2x2 TensorValue
+g = TensorValue(32,41,3,14) # creates another 2x2 TensorValue
+B = fill(t,(1,5))
+C = inner.(g,B) # inner product of g against all TensorValues in the array B
+@show C
+# C = [2494 2494 2494 2494 2494]
+```
+
+## Installation
+```julia
+Pkg.add("TensorValues)
+```