ML in Go with a Python sidecar

Automatic Differentiation (AD) is an important algorithm for calculating the derivatives of arbitrary functions that can be expressed by a computer program. One of my favorite CS papers is "Automatic differentiation in machine learning: a survey" by Baydin, Perlmutter, Radul and Siskind (ADIMLAS from here on). While this post attempts …

"Dr. Euler's Fabulous Formula" by Paul J. Nahin - a kind of sequel to the previous book I read by this author ("An imaginary tale"). Here he collected all the interesting mathematical explorations that didn't make the cut for that book. I found this one to be much closer to a …

This is Part 5 in a series of posts describing the Raft distributed consensus algorithm and its complete implementation in Go. Here is a list of posts in the series: Part 0: Introduction Part 1: Elections Part 2: Commands and log replication Part 3: Persistence and optimizations Part 4: Key …

In the previous post I talked about running ML inference in Go through a Python sidecar process. In this post, let's see how we can accomplish the same tasks without using Python at all. How ML models are implemented Let's start with a brief overview of how ML models are …

Go 1.23 shipped with a new major feature: ranging over functions (also known as "iterators"), per this proposal. This feature is nicely covered in the official Go blog post from August. This article is a rewrite of my older post that described this feature when it was still in …