George Shakan

I believe that the way mathematicians conduct research will be fundamentally different in the near future.

In this post we will see that Log loss and AUC for binary classification problems are closely related.

Principal Component Analysis, or PCA, is a fundamental dimensionality reduction technique using in Machine Learning.

We all have some problem we are trying to solve.

In a recent YouTube Video I discuss how a cool feature from Amazon could be implemented with Large Language Models.

In this somewhat different post, I am hosting the long-running Carnival of Mathematics.

In Machine Learning, and in particular Generative AI, temperature is a useful hyperparameter for tuning model outputs.

Principal Component Analysis (PCA) is a popular technique in machine learning for dimension reduction.

In the first equation of the popular Attention is all you need paper (see also this blog post), the authors write In this post we are going to discuss where the comes from, leading us to some classical Probability Theory.

How would ChatGPT read the infamous “Hello, World!”