The universal recommender in Action(ML)
04/19/21
This article presents a new collaborative filtering technique. The universal recommender can be run as “engine” of ActionML's Harness. Read more
04/19/21
This article presents a new collaborative filtering technique. The universal recommender can be run as “engine” of ActionML's Harness. Read more
05/28/20
While doing AI is sexy and cool, data infrastructure is not considered any of this, even though machine learning applications rely on it. Read more
04/21/20
By using a representative test set and various metrics, machine learning models can be evaluated and compared. Read more
09/06/19
Once a model has been trained, it can be evaluated in different ways and with more or less complex and meaningful procedures and machine learning metrics. Read more
08/06/19
In this blog post, I want to share my aha moments with you I had during the development of my... Read more
07/16/19
In this post I will discuss building a simple recommender system for a movie database which will suggest top movies and predict user votes. Read more
05/07/19
In this article, I describe the idea behind the retrievability metric which can be used to measure bias in search results. Read more
03/27/19
How to use heuristic algorithms to solve complicated optimization problems: Can you win the stacking challenge? Read more
03/19/19
Join me as I go back to the drawing board and think about approaches to plan an ML pipeline that fits your organization’s needs. We'll look at goals, technical approaches and an example architecture. Read more