Uploaded February 2021 | Updated September 2026, 2 weeks ago
NOTE: This is a new version in HD of my video from 2007. A brand new video is expected in next month.
A visual demonstration of the kernel trick in SVM.
This short video demonstrates how vectors of two classes that cannot be linearly separated in 2-D space,
can become linearly separated by a transformation function into a higher
dimensional space.
The transformation used is:
f([x y]) = [x y (x^2+y^2)]
Visit my homepage udiprod.com
NOTE: This is a new version in HD of my video from 2007. A brand new video is expected in next month.
A visual demonstration of the kernel trick in SVM.
This short video demonstrates how vectors of two classes that cannot be linearly separated in 2-D space,
can become linearly separated by a transformation function into a higher
dimensional space.
The transformation used is:
f([x y]) = [x y (x^2+y^2)]
Visit my homepage udiprod.com





![Visualization of tensors - part 1
This video series visualizes tensors using a unique and original visualization of a sphere with arrows.
Part 1 introduces the concept using the Cauchy stress tensor.
Note that this series talks about the term tensor as used in physics and math. In the field of AI the term tensor was borrowed with a simplified meaning. In AI it simply means a multi-dimensional array. So for example the array double a[4][6] (4 by 6 matrix of doubles) is called a second-order tensor, but theres no special relationship to vector spaces, as shown in the video. Visualization of tensors - part 1](https://i.ytimg.com/vi/YxXyN2ifK8A/mqdefault.jpg)




