This repository provides simple examples of how to construct a configuration space using the ConfigSpace package, how to use BOHB with minimal efforts and how to run CAVE to generate a comprehensive ...
This example jupyter notebook on Google Colab provides a walkthrough of ESCHR analysis using an example scRNA-seq dataset. If you launch the notebook in Google Colab ...
Self-driving laboratories (SDLs), powered by robotics, automation and artificial intelligence, accelerate scientific discoveries through autonomous experimentation. However, their adoption and ...
ProcessOptimizer is a Python package designed to provide easy access to advanced machine learning techniques, specifically Bayesian optimization using, e.g., Gaussian processes. Aimed at ...
Hyperparameter tuning is a critical step in optimizing machine learning models for optimal performance. It involves selecting the best combination of hyperparameters, such as regularization strength, ...
Time series forecasting is a fundamental task in data science, applied statistics, and econometrics. With time series forecasting we aim to predict the future values of time series datasets. A time ...
Isotopic composition modelling is a key aspect in many environmental studies. This work presents Isocompy, an open source Python library that estimates isotopic compositions through machine learning ...
Here we developed an open-source Python-based library called Python rodent Analysis and Tracking (PyRAT). Our library analyzes tracking data to classify distinct behaviors, estimate traveled distance, ...
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