From 71f75773aa32ccc76f2d1f8e920e8fdaf1f1c229 Mon Sep 17 00:00:00 2001 From: marc <78002988+novak-99@users.noreply.github.com> Date: Fri, 3 Dec 2021 14:55:58 -0800 Subject: [PATCH] Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 5bd190d..cb9684a 100644 --- a/README.md +++ b/README.md @@ -175,4 +175,4 @@ ML++, like most frameworks, is dynamic, and constantly changing. This is especia

## Citations -Various different materials helped me along the way of creating ML++, and I would like to give credit to them here. [This](https://www.tutorialspoint.com/cplusplus-program-to-compute-determinant-of-a-matrix) article by TutorialsPoint was a big help when trying to implement the determinant of a matrix, and [this](https://www.geeksforgeeks.org/adjoint-inverse-matrix/) article by GeeksForGeeks was very helpful when trying to take the adjoint and inverse of a matrix. Lastly, I would like to thank [this](https://towardsdatascience.com/svm-implementation-from-scratch-python-2db2fc52e5c2) article by Towards Data Science which helped illustrate a practical definition of the Hinge Loss function and its gradient when optimizing with SGD. +Various different materials helped me along the way of creating ML++, and I would like to give credit to several of them here. [This](https://www.tutorialspoint.com/cplusplus-program-to-compute-determinant-of-a-matrix) article by TutorialsPoint was a big help when trying to implement the determinant of a matrix, and [this](https://www.geeksforgeeks.org/adjoint-inverse-matrix/) article by GeeksForGeeks was very helpful when trying to take the adjoint and inverse of a matrix. Lastly, I would like to thank [this](https://towardsdatascience.com/svm-implementation-from-scratch-python-2db2fc52e5c2) article by Towards Data Science which helped illustrate a practical definition of the Hinge Loss function and its gradient when optimizing with SGD.