Guest
Our speaker Timon Ruban did a quick review of different hyperparameter tuning strategies, keeping in mind the constraints faced in real-world ML projects and finished the talk with a deep dive on Population-Based Training (PBT) and a combination of Bayesian Optimization and Hyperband (BOHB) two simple but efficient algorithms for automated hyperparameter tuning. Timon graduated top of his class from ETH Zurich. During his MSc at Stanford, he dug his heels into deep learning. He helped teach Andrew Ng’s mac
