Update README.md

README.md

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 ### Plotting the functions
 This library is implemented to be as compatible as possible with all the mainstream plotting libraries. As a result, you are free to select your favourite plotting library to plot the functions in this library.
 
-Plotting a mathematical function like `rastrigin` requires evaluating the function at a rather large set of data points. This is where the vectorized implementation of the functions in this package can shine as it allows performing the evaluations in a single function call, which significantly speeds up the plotting and reduces the computation cost. To facilitate this, the library also contains a helper function, `meshgrid`, for drawing 3D plots using the vectorized implemetations of the mathematical functions in this library. Given a list of *x* and *y* coordinates and a function, this function will create a meshgrid of points, evaluates the function over the meshgrid and returns the corresponding *x*, *y* and *z* points of the meshgrid that can be plotted with any plotting library.
+Plotting a mathematical function like `rastrigin` requires evaluating the function at a rather large set of data points. This is where the vectorized implementation of the functions in this package can shine as it allows performing the evaluations in a single function call, which significantly speeds up the plotting and reduces the computation cost. To facilitate this, the library also contains a helper function, `meshgrid`, for drawing 3D plots using the vectorized implemetations of the mathematical functions in this library. Given a list of *x* and *y* coordinates and a function, this function creates a meshgrid of points, evaluates the function over the meshgrid and returns the corresponding *x*, *y* and *z* points of the meshgrid that can be plotted with any plotting library.
 
 ```python
 from benchmarkfcns import ackley
@@ -82,7 +82,7 @@ plt.show()
 As a reference, the repository also contains the implementation of the functions in MATLAB. To install and use the MATLAB implementation, it is just required to add the project folders to MATLAB's path. For example, to use the functions in the 'benchmarks/MATLAB' folder, just navigate to this folder with MATLAB's directory explorer or use the command `addpath` with path to the folder on your PC (e.g. `addpath /path/to/benchmarks`).
 
 # Local development and compilation
-The library is built with with [pybind11](https://pybind11.readthedocs.io/), [scikit-build-core](scikit-build-core) and [Eigen](https://eigen.tuxfamily.org/). To compile the library, you will need to have CMake version 3.15 or above installed and configured. The easiest way to compile and install the package locally is to clone the repository, e.g. `BenchmarkFcns`, and then run `pip install ./BenchmarkFcns`. Although optional, it is highly recommended to install the package into a virtual environment.
+The library is built with the [pybind11](https://pybind11.readthedocs.io/), [scikit-build-core](scikit-build-core) and [Eigen](https://eigen.tuxfamily.org/) libraries. To compile the library, you will need to have CMake version 3.15 or above installed and configured. The easiest way to compile and install the package locally is to clone the repository into a directory, e.g. `BenchmarkFcns`, and then run `pip install ./BenchmarkFcns`. Although optional, it is highly recommended to install the package into a virtual environment.
 
 # Contribution
 Any suggestions and contributions are welcomed. The best way to contribute to the library is to fork the repository, apply the contributions and create a pull request.