Welcome to the tag category page for Hyperparameter optimization!
Kaggle is an online community of data scientists and machine learning engineers. It is a subsidiary of Google and offers a no-setup, customizable, Jupyter Notebooks environment. Users can access GPUs at no cost to them and a huge repository of community published data. Kaggle allows users to find and publish datasets, explore and build models in a web-based data-science environment, work with other data scientists and machine learning engineers, and enter competitions to solve data science challenges. It can be a great learning tool for beginners, and getting a job in data science is possible through Kaggle as it helps to acquire the technical skills necessary to attract potential employers seeking data scientists. Kaggle and Github are different platforms, and while Kaggle focuses on building AI models, contributing to datasets and entering competitions, Github is more function-focused and is a hosting platform for the versioning control system called Git.
Optuna is an automatic hyperparameter optimization software framework that is designed for machine learning. It features an imperative, define-by-run style interface and supports various state-of-the-art optimization algorithms, including Bayesian optimization. Optuna enables automatic searches and finds optimal hyperparameters by trial and error, which leads to excellent performance. Moreover, it supports imperative parameter definition, which provides more flexibility, and features a pruning mechanism that monitors each trial's learning curves and determines the sets of hyperparameters that will not lead to good results. Currently, the software can be used in Python and is available on GitHub. Overall, Optuna is a powerful tool that is widely used and highly recommended for hyperparameter optimization in the field of machine learning.
