readme update

README.md

@@ -52,10 +52,12 @@ Deep Learning
   - Zero Padding Layer
 
 - Models
-  - [Neural Network](https://github.com/siAyush/ScratchML/blob/main/scratch_ml/demo/neural_network.py)
+
+  - [Autoencoder](https://github.com/siAyush/ScratchML/blob/main/scratch_ml/demo/autoencoder.py)
   - [Convolutional Neural Network](https://github.com/siAyush/ScratchML/blob/main/scratch_ml/demo/convolutional_neural_network.py)
-  - [Recurrent Neural Network](https://github.com/siAyush/ScratchML/blob/main/scratch_ml/demo/recurrent_neural_network.py)
   - [Generative Adversarial Network](https://github.com/siAyush/ScratchML/blob/main/scratch_ml/demo/generative_adversarial_network.py)
+  - [Neural Network](https://github.com/siAyush/ScratchML/blob/main/scratch_ml/demo/neural_network.py)
+  - [Recurrent Neural Network](https://github.com/siAyush/ScratchML/blob/main/scratch_ml/demo/recurrent_neural_network.py)
 
 ## Installation
 
@@ -109,7 +111,7 @@ $ python3 scratch_ml/demo/neural_network.py
 
 ![Neural Network](./images/neural_network_error.png)
 
-### Convolutional Neural Network 
+### Convolutional Neural Network
 
 ```
 $ python3 scratch_ml/demo/convolutional_neural_network.py

scratch_ml/demo/autoencoder.py

@@ -0,0 +1,25 @@
+import math
+import numpy as np
+from sklearn import datasets
+import matplotlib.pyplot as plt
+from scratch_ml.deep_learning.optimizers import Adam
+from scratch_ml.deep_learning import NeuralNetwork
+from scratch_ml.deep_learning.layers import Dense, Dropout, Conv2D, Flatten, Activation, BatchNormalization
+from scratch_ml.utils import to_categorical, train_test_split, CrossEntropy
+
+
+class Autoencoder():
+    """Autoencoder with deep fully-connected neural networks."""
+
+    def __init__(self):
+        pass
+
+    def encoder():
+        pass
+
+    def decoder():
+        pass
+
+
+if __name__ == '__main__':
+    ae = Autoencoder()

scratch_ml/demo/generative_adversarial_network.py

@@ -9,7 +9,7 @@
 
 
 class GAN():
-    """Generative Adversarial Network with fully connected neural nets as
+    """Generative Adversarial Network with fully connected neural networks as
     Generator and Discriminator."""
 
     def __init__(self):