/explore
|
You are here 
|
nlml.github.io | ||
| | | | |
janakiev.com
There are many ways to compare countries and cities and many measurements to choose from. We can see how they perform economically, or how their demographics differ, but what if we take a look at data available in OpenStreetMap? In this article, we explore just that with the help of a procedure called t-SNE. |
|
| | | | | ||
| | | | |
fa.bianp.net
The Langevin algorithm is a simple and powerful method to sample from a probability distribution. It's a key ingredient of some machine learning methods such as diffusion models and differentially private learning. In this post, I'll derive a simple convergence analysis of this method in the special case when the ... |
|
| | | | | ||
| | | | |
www.oranlooney.com
In the previous article in this series we distinguished between two kinds of unsupervised learning (cluster analysis and dimensionality reduction) and discussed the former in some detail. In this installment we turn our attention to the later. In dimensionality reduction we seek a function \(f : \mathbb{R}^n \mapsto \mathbb{R}^m\) where \(n\) is the dimension of the original data \(\mathbf{X}\) and \(m\) is less than or equal to \(n\). That is, we want to map some high dimensional space into some lower dimensional space. |
|
| | | | | ||
| | | | |
programmathically.com
Sharing is caringTweetIn this post, we develop an understanding of why gradients can vanish or explode when training deep neural networks. Furthermore, we look at some strategies for avoiding exploding and vanishing gradients. The vanishing gradient problem describes a situation encountered in the training of neural networks where the gradients used to update the weights [] |
|
| | | |||
