/explore
|
You are here 
|
0fps.net | ||
| | | | |
www.jeremykun.com
|
|
| | | | | In our last primer we saw the Fourier series, which flushed out the notion that a periodic function can be represented as an infinite series of sines and cosines. While this is fine and dandy, and quite a powerful tool, it does not suffice for the real world. In the real world, very little is truly periodic, especially since human measurements can only record a finite period of time. Even things we wish to explore on this blog are hardly periodic (for instance, image analysis). | |
| | | | |
grigory.github.io
|
|
| | | | | Collection of interesting papers on algorithms for big data from 2016. | |
| | | | |
blog.christianperone.com
|
|
| | | | | We are so used to Euclidean geometry that we often overlook the significance of curved geometries and the methods for measuring things that don't reside on orthonormal bases. Just as understanding physics and the curvature of spacetime requires Riemannian geometry, I believe a profound comprehension of Machine Learning (ML) and data is also not possible | |
| | | | |
stephenmalina.com
|
|
| | | Matrix Potpourri # As part of reviewing Linear Algebra for my Machine Learning class, I've noticed there's a bunch of matrix terminology that I didn't encounter during my proof-based self-study of LA from Linear Algebra Done Right. This post is mostly intended to consolidate my own understanding and to act as a reference to future me, but if it also helps others in a similar position, that's even better! | ||
