Imagine stepping into a live casino where every move, every shuffle, and every decision is powered by cutting-edge technology. Machine […]
Read More
Revolutionizing the iGaming Arena: The Impact of Data Development
In the dynamic world of iGaming, data has emerged as a game-changer. No longer are operators relying on simple metrics […]
Read More
Revolutionizing Live Casinos: The Dynamic Role of Machine Learning
Imagine stepping into the electrifying world of live casinos, but with a twist. The dealer knows your favorite games, the […]
Read More
Feature Selection with a Scikit-Learn Pipeline
However, one major drawback is the lack of seamless integration with certain scikit-learn modules, particularly feature selection.
Read More
Adding Dask and Jupyter to a Kubernetes Cluster
Today, we’re diving into setting up Dask and Jupyter on a Kubernetes cluster hosted on AWS. If you haven’t already got a Kubernetes cluster up and running
Read More
Interpreting Machine Learning Algorithms
Understanding and interpreting machine learning algorithms can be a challenging task, especially when dealing with nonlinear and non-monotonic response functions.
Read More
Setting Up a Kubernetes Cluster on AWS in 5 Minutes
Creating a Kubernetes cluster on AWS may seem like a daunting task, but with the right guidance, it can be accomplished in just a few minutes.
Read More
I Was on a Machine Learning for Geosciences Podcast
I recently had the pleasure of being a guest on a machine learning podcast called Undersampled Radio, and it was a blast! Hosted by Gram Ganssle and Matt Hall
Read More
Autoencoders with Keras
Autoencoders have become an intriguing tool for data compression, and implementing them in Keras is surprisingly straightforward. In this post
Read More
Building Scikit-Learn Pipelines With Pandas DataFrames
Working with scikit-learn alongside pandas DataFrames has often been a source of frustration due to the lack of seamless integration between the two.
Read More