diff --git a/Readme.md b/Readme.md
index 425de04..5e24f5b 100644
--- a/Readme.md
+++ b/Readme.md
@@ -5,7 +5,7 @@
 
 [![GitHub license](https://img.shields.io/github/license/helblazer811/ManimMachineLearning)](https://github.com/helblazer811/ManimMachineLearning/blob/main/LICENSE.md)
 [![GitHub tag](https://img.shields.io/github/v/release/helblazer811/ManimMachineLearning)](https://img.shields.io/github/v/release/helblazer811/ManimMachineLearning)
-[![Github All releases](https://img.shields.io/github/downloads/helblazer811/ManimMachineLearning/total)](https://GitHub.com/helblazer811/ManimMachineLearning/releases/)
+![Pypi Downloads](https://img.shields.io/pypi/dm/manim-ml)
 [![Follow Twitter](https://img.shields.io/twitter/follow/alec_helbling?style=social)](https://twitter.com/alec_helbling)
 
 Manim Machine Learning is a project focused on providing animations and visualizations of common machine learning concepts with the [Manim Community Library](https://www.manim.community/). We want this project to be a compilation of primitive visualizations that can be easily combined to create videos about complex machine learning concepts. Additionally, we want to provide a set of abstractions which allow users to focus on explanations instead of software engineering.
@@ -16,7 +16,12 @@ Manim Machine Learning is a project focused on providing animations and visualiz
 2. [Examples](#examples)
 
 ## Getting Started 
-First you will want to [install manim](https://docs.manim.community/en/stable/installation.html). Then you can run the following to generate the example videos. 
+First you will want to [install manim](https://docs.manim.community/en/stable/installation.html). 
+
+Then install the package form source or
+`pip install manim_ml`
+
+Then you can run the following to generate the example videos from python scripts. 
 
 `manim -pqh src/vae.py VAEScene`
 
@@ -24,20 +29,49 @@ First you will want to [install manim](https://docs.manim.community/en/stable/in
 
 Checkout the ```examples``` directory for some example videos with source code. 
 
+### Neural Networks
+
+This is a visualization of a Neural Network made using ManimML. It has a Pytorch style list of layers that can be composed in arbitrary order. The following video is made with the code from below.  
+
+<img src="examples/media/ImageNeuralNetworkScene.gif">
+
+```python
+from manim import *
+from manim_ml.neural_network.layers import FeedForwardLayer, ImageLayer
+from manim_ml.neural_network.neural_network import NeuralNetwork
+from PIL import Image
+import numpy as np
+
+class ImageNeuralNetworkScene(Scene):
+
+    def construct(self):
+        image = Image.open('images/image.jpeg')
+        numpy_image = np.asarray(image)
+        # Make nn
+        layers = [
+            ImageLayer(numpy_image, height=1.0),
+            FeedForwardLayer(3), 
+            FeedForwardLayer(5),
+            FeedForwardLayer(3)
+        ]
+        nn = NeuralNetwork(layers)
+        # Center the nn
+        nn.move_to(ORIGIN)
+        self.add(nn)
+        # Play animation
+        self.play(nn.make_forward_pass_animation())
+```
+
+
 ### Variational Autoencoders
 
 This is a visualization of a Variational Autoencoder. 
 
-<img src="examples/media/VAEScene.gif" width="600">
+<img src="examples/media/VAEScene.gif">
 
 ### VAE Disentanglement 
 
 This is a visualization of disentanglement with a Variational Autoencoder
 
-<img src="examples/media/DisentanglementScene.gif" width="600">
+<img src="examples/media/DisentanglementScene.gif">
 
-### Neural Networks
-
-This is a visualization of a Neural Network. 
-
-<img src="examples/media/TestNeuralNetworkScene.gif" width="600">
diff --git a/examples/media/ImageNeuralNetworkScene.gif b/examples/media/ImageNeuralNetworkScene.gif
new file mode 100644
index 0000000..eaa1ad8
Binary files /dev/null and b/examples/media/ImageNeuralNetworkScene.gif differ
diff --git a/tests/test_neural_network.py b/tests/test_neural_network.py
index b0beed8..0b662ad 100644
--- a/tests/test_neural_network.py
+++ b/tests/test_neural_network.py
@@ -44,7 +44,6 @@ class ImageNeuralNetworkScene(Scene):
             ImageLayer(numpy_image, height=1.0),
             FeedForwardLayer(3), 
             FeedForwardLayer(5),
-            FeedForwardLayer(3),
             FeedForwardLayer(3)
         ]
         nn = NeuralNetwork(layers)