Sports Analytics Weekly by kubeia.io - 20/2023
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🎲 Betting
Ivan Toney’s betting ban shames the whole of football, not just the Brentford striker
theathletic.com
Toney admitted the offences and will be banned until January 16 next year. He will not even be allowed to train with his club until mid-September. Brentford, on the face of it, are fully entitled to be horrified about losing their best player. What has not been disclosed, however, is what brought Toney to this position and the mitigating circumstances, if there are any. Every footballer knows there are rules in place and consequences for those who ignore them. So what was it? Boredom? Recklessness? More money than sense? Or had he been sucked in, too?
Gambling firm allegedly paid blogs to link new mothers to its online games
theguardian.com
Coral had deals with parenting bloggers to offer tips, including on how to relieve stress of caring for a baby, claims source.
⚽ Sports
Chelsea's season from hell | Twitter
twitter.com
When Roman Abramovich sold Chelsea, the fans feared the worst. And it turns out they were right! Todd Boehly's first season in charge has been an absolute shitshow.
👁️ Computer Vision
Meet YOLO-NAS: New YOLO Object Detection Model Beats YOLOv6 & YOLOv8
learnopencv.com
Deep learning firm Deci.ai has recently launched YOLO-NAS. This deep learning model delivers superior real-time object detection capabilities and high performance ready for production.
💰 Quantitative Finance
Ken Griffin’s Hand-Picked Math Prodigy Runs Market-Making Empire
bloomberg.com
Citadel Securities CEO Peng Zhao left for college at age 14, caught Griffin’s eye early in his career and built systems now mopping up market share
🤖 Machine Learning
What are Large Language Models
machinelearningmastery.com - Adrian Tam
A large language model is a trained deep-learning model that understands and generates text in a human-like fashion. Behind the scene, it is a large transformer model that does all the magic.
🚀 Engineering
The simple joys of scaling up
motherduck.com - Jordan Tigani
“Do the conditions that drove our scaling challenges in 2003 still exist?” After all, we’ve made our systems far more complex and added a lot of overhead. Is it all still necessary? If you can do the job on a single machine, isn’t that going to be a better alternative? This post will dig into why scale-out became so dominant, take a look at whether those rationales still hold, and then explore some advantages of scale-up architecture.