Uploaded October 2021 | Updated September 2026, 1 day ago
Here we talk about fixed point iteration, Loki, and time loops, and we prove the Banach Fixed Point Theorem. Fixed point iteration is a tool in numerical analysis for discovering solutions to differential equations, uncovering fractal patterns, proving the convergence of Newton's method. And it's used by the Time Variance Authority to punish Loki.
Music:
Don't You Worry 'Bout Me - Katnip
Quiet in the Garden - Justnormal
Place Called Home - Jones Meadow
Wrong Team - Sture Zutterberg
Opposite - Jones Meadow
Before the Dawn - Wild Beaches
Here we talk about fixed point iteration, Loki, and time loops, and we prove the Banach Fixed Point Theorem. Fixed point iteration is a tool in numerical analysis for discovering solutions to differential equations, uncovering fractal patterns, proving the convergence of Newton's method. And it's used by the Time Variance Authority to punish Loki.
Music:
Don't You Worry 'Bout Me - Katnip
Quiet in the Garden - Justnormal
Place Called Home - Jones Meadow
Wrong Team - Sture Zutterberg
Opposite - Jones Meadow
Before the Dawn - Wild Beaches




![Getting caught with facial recognition - How SVDs are used in Facial Recognition Software
This video builds on the SVD concepts of the previous videos, where I talk about the algorithm from the paper Eigenfaces for Recognition. These tools are used everywhere from law enforcement (such as tracking down the rioters at the Capitol) to unlocking your cell phone.
Followup Video: https://youtu.be/WnsKGBy1PXQ
Music:
Come 2gether by Ooyy
Guardians + Tek by Craig Hardgrove
Images:
Lectern Photo by Win McNamee/Getty Images (Used under Fair Use for an educational video)
Face Data obtained from http://www.databookuw.com/ the readme states
This data is modified from the Extended Yale Face Database B. If using this data, please also cite the following two papers:
[1] A. S. Georghiades, P. N. Belhumeur, and D. J. Kriegman, From few to many: Illumination cone models for face recognition under variable lighting and pose, IEEE Trans. Pattern Anal. Mach. Intell., 23 (2001), pp. 643–660.
[2] K. Lee, J. Ho, and D. J. Kriegman, Acquiring linear subspaces for face recognition under variable lighting, IEEE Trans. Pattern Anal. Mach. Intell., 27 (2005), pp. 684–698.
Link to website: vision.ucsd.edu/content/extended-yale-face-database-b-b Getting caught with facial recognition - How SVDs are used in Facial Recognition Software](https://i.ytimg.com/vi/b-5jg5VvUEo/mqdefault.jpg)





