✨ 2021 rewrite - init commit

MattMoony · Sep 9, 2021 · 86ae5cb · 86ae5cb
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diff --git a/.gitignore b/.gitignore
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-t10k-*
-train-*
+data/
diff --git a/LICENSE b/LICENSE
diff --git a/README.md b/README.md
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-# ANN - MNIST Fashion Dataset
-_Simple Neural Net classifying clothing_
+<div align="center">
+    <img src="media/predictions.gif" />
+</div>
+
+
+<h1 align="center">MNIST Handwritten Digits:<br />A Simple ANN Classifier</h1>
+
+<p align="center">
+  <img alt="GitHub" src="https://img.shields.io/github/license/MattMoony/ann_mnist?style=for-the-badge">
+  <img alt="GitHub top language" src="https://img.shields.io/github/languages/top/MattMoony/ann_mnist?style=for-the-badge">
+  <img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/MattMoony/ann_mnist?style=for-the-badge&color=cecece">
+</p>
+
+---
+
+A (hopefully) well-structured and easy to understand Jupyter notebook outlining some machine-learning basics with the help of a self-written adjustable *neural network* using purely the power of the brilliant [NumPy](https://numpy.org/) module. I originally decided against using any mainstream machine-learning modules such as [TensorFlow](https://github.com/tensorflow/tensorflow), [PyTorch](https://github.com/pytorch/pytorch), [Caffe](https://github.com/BVLC/caffe), etc. in order to acquire a *deeper understanding* about the mathematics and tech behind deep neural networks.
+
+> Check out the notebook **[here](main.ipynb)!**
 
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-_TO-DO:_ Add README content
+... Matthias Monschein (September 2021)
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-    t10k-*
-    train-*
+    data/