At the end of the afternoon a friend of Esme brought us a box of 10 mangoes.
Follow 5 centering techniques as they go through a series of tests to see which one is the most resilient to change.
Source: Centering in CSS, an article by Adam Argyle.
Welcome to the third post in our series on Python at scale at Instagram! As we mentioned in the first post in the series, Instagram Server is a several-million-line Python monolith, and it moves quickly: hundreds of commits each day, deployed to production every few minutes.
We’ve run into a few pain points working with Python at that scale and speed. This article takes a look at a few that we imagine might impact others as well.
Source: Python at Scale: Strict Modules, an article by Carl Meyer.
Bayesian Decision Theory is the statistical approach to pattern classification. It leverages probability to make classifications, and measures the risk (i.e. cost) of assigning an input to a given class.
In this article we'll start by taking a look at prior probability, and how it is not an efficient way of making predictions. Bayesian Decision Theory makes better predictions by using the prior probability, likelihood probability, and evidence to calculate the posterior probability. We'll discuss all of these concepts in detail. Finally, we'll map these concepts from Bayesian Decision Theory to their context in machine learning.
Source: Introduction to Bayesian Decision Theory, an article by Ahmed Fawzy Gad.