krasserm/bayesian-machine-learning
Notebooks about Bayesian methods for machine learning
This collection of notebooks helps machine learning practitioners understand and implement Bayesian methods. It takes raw data, such as sensor readings or survey responses, and processes it through various Bayesian models like regression, classification, and optimization. The output includes predictions with quantified uncertainty and optimized parameters, which are crucial for data scientists, statisticians, and researchers building robust predictive systems.
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Use this if you need to understand the principles and practical implementations of Bayesian machine learning, especially when robust uncertainty quantification and optimized model parameters are critical for your data analysis or predictive modeling tasks.
Not ideal if you are looking for a plug-and-play solution without needing to delve into the underlying mathematical and programmatic details of Bayesian methods.
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Apache-2.0
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Mar 06, 2024
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