What is a neural network (simplified) in AI for Everyone - Hands-On Activity

What is a neural network (simplified)

📖 Scenario: You want to understand how a simple neural network works by building a basic model step-by-step. Imagine it like a small decision helper that learns from examples.

🎯 Goal: Build a simple representation of a neural network with input values, weights, and an output calculation to see how inputs influence the result.

📋 What You'll Learn

Create a list of input values representing features

Create a list of weights corresponding to each input

Calculate the weighted sum of inputs and weights

Add a bias value to the weighted sum to complete the output calculation

💡 Why This Matters

🌍 Real World

Neural networks help computers recognize patterns like images, sounds, or text by combining many inputs with weights and biases.

💼 Career

Understanding this basic concept is important for anyone interested in AI, machine learning, or data science jobs.

1

Set up the input values

Create a list called inputs with these exact values: 0.5, 0.3, and 0.2 representing input features.

AI for Everyone

# Create the list of inputs
# Your code here

Need a hint?

Think of inputs as numbers that tell the network what information it receives.

2

Set up the weights

Create a list called weights with these exact values: 0.4, 0.7, and -0.2 representing the importance of each input.

AI for Everyone

inputs = [0.5, 0.3, 0.2]
# Create the list of weights
# Your code here

Need a hint?

Weights tell the network how much each input matters.

3

Calculate the weighted sum

Create a variable called weighted_sum that calculates the sum of each input multiplied by its matching weight using a for loop with variables input_value and weight iterating over zip(inputs, weights). Initialize weighted_sum to 0 before the loop.

AI for Everyone

inputs = [0.5, 0.3, 0.2]
weights = [0.4, 0.7, -0.2]
# Calculate weighted_sum
# Your code here

Need a hint?

Multiply each input by its weight and add them all together.

4

Add bias to complete output

Create a variable called bias and set it to 0.1. Then create a variable called output that adds bias to weighted_sum.

AI for Everyone

inputs = [0.5, 0.3, 0.2]
weights = [0.4, 0.7, -0.2]
weighted_sum = 0
for input_value, weight in zip(inputs, weights):
    weighted_sum += input_value * weight
# Add bias and calculate output
# Your code here

Need a hint?

Bias shifts the result to help the network learn better.