Notes for derivatives for machine learning in Jupyter Notebook
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Updated
Jun 19, 2024 - Jupyter Notebook
Notes for derivatives for machine learning in Jupyter Notebook
Summary notebooks using derivative gaussian processes with tinygp. We implement a 2D derivative gaussian process and successfully use derivatives to regularize SVI fits with a gaussian process model..
In this notebook, we build and train a Multi-Step / Multi-Output Regression Model powered by TensorFlow & Keras in order to predict Bitcoin's future trend.
Implement, demonstrate, reproduce and extend the results of the Risk articles 'Differential Machine Learning' (2020) and 'PCA with a Difference' (2021) by Huge and Savine, and cover implementation details left out from the papers.
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