diff --git a/ML Cookbook/BasicNeuralNet.py b/ML Cookbook/BasicNeuralNet.py
new file mode 100644
index 0000000..92e6d2f
--- /dev/null
+++ b/ML Cookbook/BasicNeuralNet.py	
@@ -0,0 +1,23 @@
+from torch import nn
+
+class BasicNeuralNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        
+        # Inputs to hidden layer linear transformation
+        self.hidden = nn.Linear(784, 256)
+        # Output layer, 10 units 
+        self.output = nn.Linear(256, 10)
+        
+        # Define sigmoid activation and softmax output 
+        self.sigmoid = nn.Sigmoid()
+        self.softmax = nn.Softmax(dim=1)
+        
+    def forward(self, x):
+        # Pass the input tensor through each of the operations
+        x = self.hidden(x)
+        x = self.sigmoid(x)
+        x = self.output(x)
+        x = self.softmax(x)
+        
+        return x
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diff --git a/ML Cookbook/CNNs/BasicCNN.py b/ML Cookbook/CNNs/BasicCNN.py
new file mode 100644
index 0000000..178ba57
--- /dev/null
+++ b/ML Cookbook/CNNs/BasicCNN.py	
@@ -0,0 +1,16 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+# CNN architecture definition
+class Net(nn.Module):
+    def __init__(self):
+        super(Net, self).__init__()
+        # convolutional layer
+        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
+        # max pooling layer
+        self.pool = nn.MaxPool2d(2, 2)
+
+    def forward(self, x):
+        # add sequence of convolutional and max pooling layers
+        x = self.pool(F.relu(self.conv1(x)))
+        return x
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