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@@ -11,11 +11,11 @@ These notebooks complement the Risk papers [Differential Machine Learning](https
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<ahref="https://colab.research.google.com/github/differential-machine-learning/notebooks/blob/master/DifferentialMLTF2.ipynb"target="_parent"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"/></a>
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**DifferentialRegression.ipynb** applies differential learning in the context of classic regression models. In the article, we applied differential learning to deep neural networks only. This notebook applies it to polynomial regression to the basket option in a correlated Bachelier model of section 3,1. We see that, with regression too, differential training provides a massive performance improvement, without the need for additional regularization, or hyperparameter optimization. Differential regression is covered in detail in the upcoming follow-up article <b> Axes that matter: PCA with a difference</b>.
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**DifferentialRegression.ipynb** applies differential learning in the context of classic regression models. In the article, we applied differential learning to deep neural networks only. This notebook applies it to polynomial regression to the basket option in a correlated Bachelier model of section 3,1. We see that, with regression too, differential training provides a massive performance improvement, without the need for additional regularization, or hyperparameter optimization. Differential regression is covered in detail in the upcoming follow-up article <b> Axes that matter: PCA with a difference</b>.
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<ahref="https://colab.research.google.com/github/differential-machine-learning/notebooks/blob/master/DifferentialRegression.ipynb"target="_parent"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"/></a>
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**DifferentialPCA.ipynb** implements and illustrates differential dimension reduction covered in the follow-up article <b> Axes that matter: PCA with a difference</b>.
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**DifferentialPCA.ipynb** implements and illustrates differential dimension reduction covered in the follow-up article <b> Axes that matter: PCA with a difference</b>.
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<ahref="https://colab.research.google.com/github/differential-machine-learning/notebooks/blob/master/DifferentialPCA.ipynb"target="_parent"><imgsrc="https://colab.research.google.com/assets/colab-badge.svg"alt="Open In Colab"/></a>
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