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PyTorch, TensorFlow, neural networks, CNNs, transformers, and deep learning for production

Bayesian regression models including linear, logistic, Poisson, negative binomial, and robust regression with Stan and J...

Deploy machine learning models to production using Flask, FastAPI, Docker, cloud platforms (AWS, GCP, Azure), and model...

An overview of the core model API of SeisBench, a Python framework for training and applying machine learning algorithms...

Foundational knowledge for writing Stan 2.37 models including program structure, type system, distributions, and best pr...

Measure model performance on test datasets. Use when assessing accuracy, precision, recall, and other metrics.

Calibrate GLM parameters for water temperature simulation. Use when you need to adjust model parameters to minimize RMSE...

Expert guidance for Stan probabilistic programming language development, including modern syntax, cmdstanr/cmdstanpy int...

Expert guidance for regression analysis, statistical modeling, and outlier detection in Python using statsmodels, scikit...

Optimize workload distribution across workers, processes, or nodes for efficient parallel execution. Use when asked to b...

Strategy for solving constraint optimization problems on spatial maps. Use when you need to place items on a grid/map to...

Use when creating Python projects, managing dependencies with uv, configuring pyproject.toml, building packages, or publ...