Update README.md

README.md

@@ -28,7 +28,7 @@ Math on (Hyper-Dual) Tensors with Trailing Axes.
 - No arbitrary-order gradients (only first- and second order gradients)
 
 # Why `tensortrax`?
-Compared to other libaries which introduce a new (hyper-) dual `dtype` (treated as `dtype=object` in NumPy), `tensortrax` relies on its own `Tensor` class. This approach involves a re-definition of all essential math operations (and NumPy-functions), whereas the dtype-approach supports most operations (even NumPy) out of the box. However, in `tensortrax` NumPy operates on default data types (e.g. `dtype=float`). This allows to support functions like `np.einsum()`. Beside the differences concerning the underlying `dtype`, `tensortrax` is formulated on (tensorial) calculus of variation.
+Compared to other libaries which introduce a new (hyper-) dual `dtype` (treated as `dtype=object` in NumPy), `tensortrax` relies on its own `Tensor` class. This approach involves a re-definition of all essential math operations (and NumPy-functions), whereas the dtype-approach supports most operations (even NumPy) out of the box. However, in `tensortrax` NumPy operates on default data types (e.g. `dtype=float`). This allows to support functions like `np.einsum()`. Beside the differences concerning the underlying `dtype`, `tensortrax` is formulated on (tensorial) calculus of variation. Gradient- and hessian-vector products are evaluated with very little overhead compared to analytic formulations.
 
 # Usage
 Let's define a scalar-valued function which operates on a tensor.