From 28322c86843c78128c810c795a50e413b67e1a27 Mon Sep 17 00:00:00 2001
From: marc <78002988+novak-99@users.noreply.github.com>
Date: Fri, 24 Sep 2021 17:00:59 -0700
Subject: [PATCH] Update README.md

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 README.md | 10 ++++++++++
 1 file changed, 10 insertions(+)

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@@ -151,3 +151,13 @@ The result will be the model's predictions for the entire dataset.
     3. Recall 
     4. Accuracy
     5. F1 score
+
+
+## What's in the Works? 
+ML++, like most frameworks, is dynamic, and constantly changing! This is especially important in the world of ML, as new algorithms and techniques are being developed day by day. Here a couple things currently being developed for ML++:
+    - Convolutional Neural Networks 
+    - Kernels for SVMs 
+    - Support Vector Regression
+    
+## 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/) website 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 from Towards Data Science which helped illustrate a practical definition of the Hinge Loss activation function and its gradient.