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Linear Algebra - Khan Academy
Learn linear algebra—vectors, matrices, transformations, and more. Skip to main content If you're seeing this message, it means we're having trouble loading external resources on our website.
Vector intro for linear algebra (video) | Khan Academy
A vector has both magnitude and direction. We use vectors to, for example, describe the velocity of moving objects. In this video, you'll learn how to write and draw vectors.
Vector dot product and vector length (video) | Khan Academy
Definitions of the vector dot product and vector length. Skip to main content. ... Middle school Earth and space science; Middle school physics; High school biology. NEW. High school chemistry. NEW. ... Linear algebra. Course: Linear algebra …
Vector dot product and vector length - Khan Academy
That vector dot my b vector: b1, b2, all the way down to bn is going to be equal to the product of each of their corresponding components. So a1 b1 added together plus a2 b2 plus a3 b3 plus all the way to a n, bn.
Vector triangle inequality (video) | Khan Academy
Algebra 1; Geometry; Algebra 2; Integrated math 1; Integrated math 2; Integrated math 3; Algebra basics; Trigonometry; Precalculus; High school statistics; Statistics & probability; College algebra; AP®︎/College Calculus AB; AP®︎/College Calculus BC; AP®︎/College Statistics; Multivariable calculus; Differential equations; Linear ...
More on linear independence - Khan Academy
In general if V is a n dimensional vector space, then any set of n+1 or more vectors is linearly dependent.
Proving vector dot product properties - Khan Academy
The dot product is well defined in euclidean vector spaces, but the inner product is defined such that it also function in abstract vector space, mapping the result into the Real number space. In any case, all the important properties remain:
Khan Academy
Learn about linear subspaces in this Khan Academy video, covering the concepts and properties of subspaces in linear algebra.
Introduction to linear independence - Khan Academy
And you can imagine in three space, if you have one vector that looks like this and another vector that looks like this, two vectors that aren't collinear, they're going to define a kind of two-dimensional space.