Examples

See also

To explore the trusted and explainable methods we use under the hood, explore our free knowledge base.

Tutorials

Name	Description	GitHub Link
Quickstart	Get started with twinLab.	View Jupyter Notebook
Prediction	Make predictions with your model.	View Jupyter Notebook
Recommend: Explore	Figure out how to add the most informative new data points to your training set.	View Jupyter Notebook
Recommend: Optimize	Solve an optimization problem by finding data points with your trained emulator.	View Jupyter Notebook
Noisy data	Learn how to handle noisy observations in your data using twinLab.	View Jupyter Notebook
Noiseless data	Train and use a model that assumes no noise in the observations.	View Jupyter Notebook
Model selection	How twinLab can automate the process of kernel selection and composition.	View Jupyter Notebook
Bayesian optimisation	Finding the maxima of an unknown function using twinLab.	View Jupyter Notebook
Functional decomposition	Effectively processing the training dataset.	View Jupyter Notebook
Dimensionality reduction	Truncating the number of features to accelerate your emulator’s training.	View Jupyter Notebook
Initial design	Recommendations of points for sampling before training an emulator.	View Jupyter Notebook
Mixture of experts model	Train and use a model that uses a combination of multiple GP experts.	View Jupyter Notebook
Classification	Train and use a model for binary classification problems.	View Jupyter Notebook
Solving inverse problems	Determining the underlying conditions (inputs) that led to observed outcomes.	View Jupyter Notebook

See also

Now you’ve completed our tutorials, start constructing emulators for your own problems with help from our Python documentation.

Let us know if you get stuck - we’re always happy to help!