Branch-AI/piscine/week03/day01/ex01/README.md

Exercise 1 The neuron

The goal of this exercise is to understand the role of a neuron and to implement a neuron.

An artificial neuron, the basic unit of the neural network, (also referred to as a perceptron) is a mathematical function. It takes one or more inputs that are multiplied by values called “weights” and added together. This value is then passed to a non-linear function, known as an activation function, to become the neuron’s output.

As desbribed in the article, a neuron takes inputs, does some math with them, and produces one output.

Let us assume there are 2 inputs. Here are the three steps involved in the neuron:

1. Each input is multiplied by a weight
   • x1 -> x1 * w1
   • x2 -> x2 * w2
2. The weighted inputs are added together with a biais b
   • (x1 * w1) + (x2 * w2) + b
3. The sum is passed through an activation function

   • y = f((x1 * w1) + (x2 * w2) + b)

   • The activation function is a function you know from W2DAY2 (Logistic Regression): the sigmoid

Example:

x1 = 2 , x2 = 3 , w1 = 0, w2= 1, b = 4

1. Step 1: Multiply by a weight
   • x1 -> 2 * 0 = 0
   • x2 -> 3 * 1 = 3
2. Step 2: Add weigthed inputs and bias
   • 0 + 3 + 4 = 7
3. Step 3: Activation function
   • y = f(7) = 0.999

---

1. Implement a the function feedforward of the class Neuron that takes as input the inputs (x1, x2) and that uses the attributes: the weights and the biais to return y:

   class Neuron:
   def __init__(self, weight1, weight2, bias):
       self.weights_1 = weight1
       self.weights_2 = weight2
       self.bias = bias
   
   def feedforward(cls, x1, x2):
       #TODO
       return y
   
   
   

Note: if you are confortable with matrix multiplication, feel free to vectorize the operations as done in the article.

https://victorzhou.com/blog/intro-to-neural-networks/