Tag: Boosting

Time-series forecasting is a crucial task in many domains, including finance, sales, and weather prediction. While classical timeseries models and deep learning techniques have been widely used for this purpose, there’s growing evidence that gradient boosting often outshines other methods. ...

Welcome to another edition of “The Kaggle Blueprints”, where we will analyze Kaggle competitions’ winning solutions for lessons we can apply to our own data science projects. This edition will review the techniques and approaches from the “BirdCLEF 2023&rdqu...

My goal was to understand what made users satisfied with their music experience. To do so, I built a LightGBM classifier whose output was a binary response: y = 1 → the user is seemingly satisfied y = 0 → not so much Predicting human satisfaction is a challenge because humans are by d...

Experimenting with Convolutional Neural Networks (CNNs) from scratch in Rust. In my previous article (Part 1) I started my experiment to develop a machine learning framework in Rust from scratch. The main aim of my experiment was to gauge model training speed improvements that can be attained by ...

This article is one of a two-part piece documenting my learnings from my Machine Learning Thesis at Spotify. Be sure to also check out the second article on how I implemented Feature Importance in this research. Feature Importance Analysis with SHAP I Learned at Spotify (with the Help of the...

AI is undoubtedly pushing boundaries to creativity and innovation for Designers and Creatives alike. By using AI-powered simulation tools, designers can create virtual fashion shows that allow them to experiment with different runway styles, models, and lighting conditions. AI tools are also enab...