Articles

(Jan 14) #machine learning #julia #math

The problems with classical machine learning can be roughly summarized by saying the models require lots of data to train and are quite limited to that data. But that might not be the case for long. In this technical article by Christopher Rackauckas, he introduces the universal differential equation approach, which consists of "[utilizing] all of the known scientific structure to embed as much prior knowledge as possible". Ultimately it reduces the amount of data required to train a model, results in better interpolation, and clearer interpretability.

(Jan 14) #sql #optimization

What if everything you knew about querying in SQL was wrong? Well that's not exactly true, but it appears that there might be a few cases when one of the most fundamental things you learn about using SELECT is wrong. In Brent Ozar's article, he demonstrates how due to the SQL Server underestimating a memory grant, it might be possible that selecting all of the columns from a row in a query might be faster than selecting just one.

(Jan 01) #cryptography

The world of cryptography can be, for the lack of a better word, cryptic. But understanding a basic example is a step towards mastering the art, and that's what Robert Xiao provides us in this article. In it, he presents the Sarah2 Pen-and-Paper Cipher, explains how it works, and demonstrates two attacks that can be used to decipher the key used to encrypt information. He then outlines how the cipher can be modified using subkeys to fix the vulnerabilities he outlines.

(Jan 01) #math #probability #monte carlo #python

We've had numerous articles about the Markov Chain Monte Carlo and Bayesian Inference. They've all been good in their own right, but mostly assumed that you were already fairly familiar with the topic. In Divyanshu Kalra's article, he does an excellent job of introducing these two topics from the most basic level and explaining them using simple to understand examples. This is all done to ultimately give us an understanding of how Metropolis-Hastings sampling can be used to solve complex problems.

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