Skip to main content
AI Fundamentals Tutorial
CHAPTER 06 Beginner

Introduction to Neural Networks

Updated: May 14, 2026
25 min read

# CHAPTER 6

Introduction to Neural Networks

1. Introduction

If standard Machine Learning uses statistics, Deep Learning uses biology—at least conceptually. To solve the hardest problems in computer science, engineers looked at the most advanced computer in the known universe: the human brain. They created an artificial architecture called the Artificial Neural Network (ANN). In this chapter, we will break down the basic components of neural networks, including neurons, layers, and activation functions.

2. Learning Objectives

By the end of this chapter, you will be able to:
  • Explain what an Artificial Neural Network is.
  • Identify the three main types of layers in a network.
  • Understand the role of "Weights" and "Biases."
  • Describe the purpose of an Activation Function.

3. Beginner-Friendly Explanation

Imagine an assembly line of factory workers trying to guess if an image is a dog or a cat.
  • The first row of workers looks at the raw pixels. One worker only looks for pointy ears. Another looks for fur. They shout their findings to the next row.
  • The second row listens to the first row. If they hear "pointy ears" and "fur", they shout "Probably a cat!" to the boss.
  • The boss (the output) listens to everyone and makes the final decision: "It is a cat!"
An Artificial Neural Network is exactly this: rows of mathematical "workers" (neurons) passing information forward, layer by layer, until a final decision is made.

4. Real-World Examples

  • Handwriting Recognition: When you deposit a check at an ATM, a neural network looks at your messy handwriting, passes the shapes of the ink through multiple layers, and figures out that your scribble is a "7".
  • Voice Recognition: When you say "Hey Siri," a neural network processes the audio frequencies layer by layer to determine if the sounds match the wake word.

5. Components: The Neuron (Node)

The fundamental building block of an ANN is the Neuron (also called a node). A biological neuron receives electricity from one end, decides if the shock is strong enough, and fires electricity out the other end. An artificial neuron does the same thing with math: It receives a number, multiplies it, adds a bit, and decides whether to pass a new number forward to the next neuron.

6. Components: The Layers

Neurons are organized into columns called Layers.
  1. 1. Input Layer: The first layer. It receives the raw data. (e.g., If looking at a 10x10 pixel image, there will be 100 neurons in the input layer, one for each pixel).
  1. 2. Hidden Layers: The layers in the middle. This is where the actual "thinking" and pattern recognition happens. A network can have one hidden layer, or a hundred.
  1. 3. Output Layer: The final layer. It delivers the result. (e.g., If deciding between Dog or Cat, the output layer will have 2 neurons. The one with the highest number wins).

7. Weights and Biases

How does the network actually learn? The connections between the neurons have Weights (how important is this connection?) and Biases (an extra push). When a neural network is "training," it is literally just slightly adjusting these thousands of Weight numbers up or down until the Output Layer finally starts guessing correctly.

8. Activation Functions

If a neural network only multiplied and added numbers, it would just be a simple straight line (Linear Regression). To solve complex, curvy, real-world problems, we need an Activation Function. This is a mathematical gate at the end of each neuron that decides *how much* signal to pass forward. Common activation functions include:
  • ReLU (Rectified Linear Unit): Very common. If the number is negative, it outputs 0. If positive, it passes it through.
  • Sigmoid: Squeezes any number into a value between 0 and 1. Great for predicting probabilities (e.g., 0.85 = 85% chance it's a cat).

9. Mini Project

Draw a Network: Take a piece of paper. Draw 3 circles in a vertical column on the left (Input Layer). Draw 4 circles in a vertical column in the middle (Hidden Layer). Draw 2 circles on the right (Output Layer). Draw lines connecting every circle in the left column to every circle in the middle, and every middle circle to the right. You have just designed a fully connected Neural Network architecture!

10. Best Practices

  • Don't overcomplicate: You do not need a neural network to predict house prices based on square footage. Standard ML (Linear Regression) works better. Neural networks are for massive, complex, unstructured data (images, audio, text).

11. Common Mistakes

  • Treating it like a brain: While inspired by biology, an ANN is just a massive chain of calculus and matrix multiplication. It doesn't "think"—it computes probabilities based on mathematical weights.

12. Exercises

  1. 1. If you wanted to build a neural network to predict if a picture shows an Apple, an Orange, or a Banana, how many neurons must be in the Output Layer? *(Answer: 3)*

13. Coding Challenges

Challenge 1: Write pseudocode for how a single Artificial Neuron calculates its output.
text
1234567891011 
// Pseudocode for a single Neuron
Input = 5
Weight = 0.8
Bias = 2

// Step 1: Multiply and Add
Sum = (Input * Weight) + Bias  // (5 * 0.8) + 2 = 6

// Step 2: Apply Activation Function (e.g., ReLU)
Output = ReLU(Sum) 
// ReLU passes positive numbers through untouched. So Output = 6.

14. MCQs with Answers

Question 1

What is the purpose of the "Hidden Layers" in a neural network?

Question 2

What mathematical component allows a neural network to solve complex, non-linear problems instead of just drawing straight lines?

15. Interview Questions

  • Q: Explain the components of an artificial neuron.
  • Q: What is the difference between a Weight and a Bias in a neural network?

16. FAQs

Q: How does the network know it made a mistake? A: Through a process called Backpropagation. If the network guesses "Dog" but the label says "Cat," an algorithm runs *backwards* through the network, automatically tweaking the Weights so it doesn't make that mistake next time.

17. Summary

In Chapter 6, we peeked under the hood of Deep Learning. Artificial Neural Networks consist of an Input layer, Hidden layers, and an Output layer. By passing data through artificial neurons, multiplying by Weights, and passing through Activation Functions, these networks can discover incredibly complex patterns in data.

18. Next Chapter Recommendation

We know what a basic neural network is. But how do we use them for advanced tasks like self-driving cars? Proceed to Chapter 7: Understanding Deep Learning to explore advanced architectures like CNNs and RNNs.

Finish this Chapter

Save your progress on your learning path and prepare for coding interview challenges.

Previous Chapter Understanding Machine Learning Basics
Next Chapter Understanding Deep Learning

Discussion

Join the discussion

Log in or create a free account to participate.

Sort: ·

Explore More

📖 Related Tutorials 5

❓ Related Quizzes 6

🎥 Related Videos 1