Linklog

This is a very interesting dive into practical applications of reinforcement learning to optimization problems.

This is a gorgeous explanation why momentum works in gradient descent.

This is a fascinating article on dimensionality reduction and how to use local similarity structure to extract as much information as possible. The visuals are also fantastic and very engaging. A really good read!

I always love a good algorithm visualisation, and this article does a really good job at explaining the A* and Dijkstra graph exploration algorithms.

A very interesting sampling method specialized in fair selection of streaming events.

A very neat summary of what Markov chains are and how they work, with beautiful animations.

A great introduction to maze generation algorithms with informative visual.

An interesting analysis of various optimization algorithms applied to a simple dynamical programming problem. Features beautiful visualizations of those algorithms.

A short blog post summarizing the main compression algorithms. It's incredible how little I knew about something I use so much.

This a collection of all the optimization metaheuristic you can possibly imagine, with comments on how to implement them and what parameters can be tuned. The aim is to take it one algorithm per afternoon.

A beautiful set of visualizations of common algorithms. Perfect to truly understand what happens in a quicksort algorithm, or to compare different sampling algorithms.