Highlight: So far, we have already talked that it is possible to represent the vector using different basis vectors. In this post we will learn how to go from our standard coordinate system \(\left ( x,y \right ) \) into some other bases. Next, we will also learn why this change of basis can be very useful. For now, we will just say that it’s frequently applied in many signal processing and machine learning methods.…
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Highlight: In this post we will review one of the fundamental operators in Linear Algebra. It is known as a Dot product or an Inner product of two vectors. Most of you are already familiar with this operator, and actually it’s quite easy to explain. And yet, we will give some additional insights as well as some basic info how to use it in Python. Tutorial Overview: Dot product :: Definition and properties Linear functions…
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Highlight: In this post we will talk about eigenvalues and eigenvectors. This concept proved to be quite puzzling to comprehend for many machine learning and linear algebra practitioners. However, with a very good and solid introduction that we provided in our previous posts we will be able to explain eigenvalues and eigenvectors and enable very good visual interpretation and intuition of these topics. We give some Python recipes as well. Tutorial Overview: Intuition about eigenvectors…
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