In addition to enabling the definition of lengths of vectors, as well as the distance between two vectors, inner products also capture the geometry of a vector space by defining the angle $\omega$ between two vectors. Assume $x \ne 0, y \ne 0$, then $ -1 \le \frac{<x,y> }{\left \| x \right \|\left \| y\right \|}$ $ \le 1$ therefore there exist a unique $\omega \in [0,\pi]$ with $ cos \omega= \frac{<x,y> }{\left \| x \right \|\left \| y\right \|}$ The number $\omega$ is the angle between the vectors $x$ and $y$. Intuitively, the angle between two vectors tells us how similar their orientations are. For example, using the dot product, the angle between $x$ and $y = 4x$, i.e., y is a scaled version of x, is 0: Their orientation is the same. Example ( university question) Lets consider the angle between $x=[1,1]^T \in \mathbb{R}^2$ and $y=[1,2]^T \in \mathbb{R}^2$.If we use dot product as the inner product $cos \omega=\frac{<x,y>}{\sqrt{<x,x>...
This blog is written for the following two courses of KTU using python. CST284-Mathematics for Machine Learning-KTU Minor course and CST294-Computational Fundamentals for Machine Learning-KTU honors course. Queries can be send to Dr Binu V P. 9847390760