# Programming

## Writing Type-Stable Code in Julia

For many of the people I talk to, Julia’s main appeal is speed. But achieving peak performance in Julia requires that programmers absorb a few subtle concepts that are generally unfamiliar to users of weakly typed languages. One particularly subtle performance pitfall is the need to write type-stable code. Code is said to be type-stable […]

## Hopfield Networks in Julia

As a fun side project last night, I decided to implement a basic package for working with Hopfield networks in Julia. Since I suspect many of the readers of this blog have never seen a Hopfield net before, let me explain what they are and what they can be used for. The short-and-skinny is that […]

## Writing Better Statistical Programs in R

A while back a friend asked me for advice about speeding up some R code that they’d written. Because they were running an extensive Monte Carlo simulation of a model they’d been developing, the poor performance of their code had become an impediment to their work. After I looked through their code, it was clear […]

## Symbolic Differentiation in Julia

A Brief Introduction to Metaprogramming in Julia In contrast to my previous post, which described one way in which Julia allows (and expects) the programmer to write code that directly employs the atomic operations offered by computers, this post is meant to introduce newcomers to some of Julia’s higher level functions for metaprogramming. To make […]

## Computers are Machines

When people try out Julia for the first time, many of them are worried by the following example: 1 2 3 4 5 6 7 julia> factorial(n) = n == 0 ? 1 : n * factorial(n – 1)   julia> factorial(20) 2432902008176640000   julia> factorial(21) -4249290049419214848 If you’re not familiar with computer architecture, this […]

## The State of Statistics in Julia

Updated 12.2.2012: Added sample output based on a suggestion from Stefan Karpinski. Introduction Over the last few weeks, the Julia core team has rolled out a demo version of Julia’s package management system. While the Julia package system is still very much in beta, it nevertheless provides the first plausible way for non-expert users to […]

## Finder Bug in OS X

Four years after I first noticed it, Finder still has a bug in it that causes it to report a negative number of items waiting for deletion:

## My New Book: Developing, Deploying and Debugging Multi-Armed Bandit Algorithms

I’m happy to announce that I’ve started writing a new book for O’Reilly, which will focus on teaching readers how to use Multi-Armed Bandit Algorithms to build better websites. My hope is that the book can help web developers build up an intuition for the core conundrum facing anyone who wants to build a successful […]

## Automatic Hyperparameter Tuning Methods

At MSR this week, we had two very good talks on algorithmic methods for tuning the hyperparameters of machine learning models. Selecting appropriate settings for hyperparameters is a constant problem in machine learning, which is somewhat surprising given how much expertise the machine learning community has in optimization theory. I suspect there’s interesting psychological and […]

## Optimization Functions in Julia

Update 10/30/2013: Since this post was written, Julia has acquired a large body of optimization tools, which have been grouped under the heading of JuliaOpt. Over the last few weeks, I’ve made a concerted effort to develop a basic suite of optimization algorithms for Julia so that Matlab programmers used to using fminunc() and R […]