updated README and added documentation for #1 Single Layer Perceptrons

	modified:   README.md
	modified:   perceptrons.ipynb

README.md

@@ -2,6 +2,40 @@
 
 This project implements artificial neural networks (ANN) using perceptrons as per the ANN university subject.
 
-1. Single Layer
+0. [Sum Function](#0-sum-function)
+1. [Single Layer](#1-single-layer-perceptron)
 2. Single Layer and Training
-3. Multi Layer
+3. Multi Layer
+
+## 0. Sum Function
+    The sum_function is designed to compute the sum of the products of corresponding elements from two lists: inputs and weights. This is a common operation in neural networks, particularly in the calculation of the net input to a neuron.
+
+```python
+def sum_function(inputs, weights) -> float:
+    '''
+    Sum of the product of the inputs by the weights
+
+    @return float: The Net Input / Pre-Activation result 
+    '''
+
+    ...
+```
+
+## 1. Single Layer Perceptron
+    In this section, we implement a simple step function, which is often used in academic settings to illustrate the basic principles of neural networks and perceptrons.
+
+```python
+def step_function(net_input) -> int:
+    '''
+    1, if net_input >= 1
+    0, if net_input < 1
+    
+    @return int: The Activation result
+    '''
+
+    ...
+```
+> The following chart is made using both the `Sum` and `Step` functions
+
+![Step Function Plot](step_function_plot.png)
+