OranLooney.com

Richard Hamming famously used to ask his colleagues at Bell Labs this question: “What is the most important problem in your field, and why aren’t you working on it?”

What’s your hot take on relational databases?

This essay isn’t to explain the solution to the Monty Hall problem—you can look that up anywhere—but to ask a related question: why does it seem to drive some people crazy?

We are in a golden age of grift.

I bring you news of the single most important intellectual discovery of our generation: the hard problem of human consciousness has been solved.

In part I of this two-part series we covered lookup tables and simple devices with at most a handful of moving parts.

What is a computer, really?

You might think it’s unlikely for any interesting mathematics to arise from incense appreciation, but that’s only because you’re unfamiliar with the peculiar character of Muromachi (室町) era Japanese nobles.

Here’s a fact: GPT-4o charges 170 tokens to process each 512x512 tile used in high-res mode.

How good are LLMs at trivia?

One thing you may have noticed about the trigonometric functions sine and cosine is that they seem to have no agreed upon definition.

Kaprekar’s routine is a simple arithmetic procedure on four digit numbers which rapidly converges to the fixed point 6174, known as the Kaprekar constant.

In 2020, the Zodiac 340 cipher was finally cracked after more than 50 years of trying by amateur code breakers.

It's hard to talk about ChatGPT without cherry-picking.

To celebrate the 100th anniversary of the birth of encabulation - dated from Dr.

Today is the last day when the number of people alive will start with a seven.

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.

I recently wrote an article which was ostensibly about the Fibonacci series but was really about optimization techniques.

Consider the following motivating dataset: It is apparent that these data have some kind of structure; which is to say, they certainly are not drawn from a uniform or other simple distribution.

Today, let me be vague.

So far in this series we’ve followed one particular thread: linear regression -> logistic regression -> neural network.

A common example of recursion is the function to calculate the $n$-th Fibonacci number: def naive_fib(n): if n < 2: return n else: return naive_fib(n-1) + naive_fib(n-2) This follows the mathematical definition very closely but it’s performance is terribl…

In today’s installment of Machine Learning From Scratch we’ll build on the logistic regression from last time to create a classifier which is able to automatically represent non-linear relationships and interactions between features: the neural network.

In this second installment of the machine learning from scratch we switch the point of view from regression to classification: instead of estimating a number, we will be trying to guess which of 2 possible classes a given input belongs to.

To kick off this series, will start with something simple yet foundational: linear regression via ordinary least squares.

Motivation As an apprentice, every new magician must prove to his own satisfaction, at least once, that there is truly great power in magic.

Introduction Visualizing the results of a binary classifier is already a challenge, but having more than two classes aggravates the matter considerably.

Craps is a suprisingly fair game.

This post is part of a series on complex number functionality in the R programming language.

R, like many scientific programming languages, has first-class support for complex numbers.

Last week my boss stopped by and dropped a brand spanking new iPad on my desk.

Update 2017-10-23: This article and code library have not kept up with the rapidly changing JavaScript landscape and are now hopelessly out of date.

se-man-tic (si-man’tik) adj. 1.