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Graphical representation is also very helpful to understand linear algebra. I tried to bind the concepts with plots (and code to produce it). The type of representation I liked most by doing this series is the fact that you can see any matrix as linear transformation of the space. In several chapters we will extend this idea and see how it can be useful to understand eigendecomposition, Singular Value Decomposition (SVD) or the Principal Components Analysis (PCA). In addition, I noticed that creating and reading examples is really helpful to understand the theory. It is why I built Python notebooks.