Skip to main content

Neural Networks

Objective​

This chapter explains neural networks in a simple and understandable way. You will learn what they are, how they work, and how they mimic the human brain.

1. What are Neural Networks?​

Simple Definition​

Neural networks are a way to simulate how the human brain works using a computer.

Simple Example:

  • Human brain: billions of connected neurons
  • Neural network: thousands or millions of connected "cells"
  • Simpler than the brain but works on the same principle

Simple Analogy​

Human Brain:

  • Neurons connected to each other
  • Each neuron receives and sends information
  • Together they form thought

Neural Network:

  • Digital "cells" connected to each other
  • Each "cell" receives and sends information
  • Together they solve problems

2. How Do They Work?​

Basic Components​

1. Neurons:

  • Basic unit
  • Like a neuron in the brain
  • Receives and sends information

2. Connections:

  • Connect neurons to each other
  • Each connection has a weight (strength)
  • Weights control the information

3. Layers:

  • Neurons organized in layers
  • Input layer: receives data
  • Hidden layers: process data
  • Output layer: gives the result

3. Simple Process​

How Do They Process Information?​

Step 1: Input

  • Data enters the first layer
  • Example: a picture of a cat

Step 2: Processing

  • Each layer processes the information
  • Layer 1: detects lines
  • Layer 2: detects shapes
  • Layer 3: detects parts

Step 3: Output

  • The last layer gives the result
  • Example: "This is a cat" with a confidence level

4. How Do They Learn?​

Learning Process​

1. First Attempt:

  • The network tries
  • The result may be wrong

2. Comparison:

  • Compare the result with the correct answer
  • Calculate the error

3. Correction:

  • Adjust the weights (strength)
  • To make the result better

4. Repetition:

  • Repeat the process
  • With each attempt, it improves

5. Simple Example: Digit Recognition​

How Does It Work?​

Input:

  • Image of a digit (e.g., 5)
  • 28x28 pixels

Layer 1:

  • Detects simple lines
  • Horizontal, vertical, curved lines

Layer 2:

  • Detects shapes
  • Circles, straight lines

Layer 3:

  • Detects digits
  • Shape of the number 5

Output:

  • "This number is 5"
  • With 95% confidence

6. Types of Neural Networks​

Simple Neural Network​

Characteristics:

  • Few layers
  • Simple
  • Suitable for simple problems

Example:

  • Price prediction
  • Simple classification

Deep Neural Network​

Characteristics:

  • Many layers
  • Complex
  • Suitable for complex problems

Example:

  • Image recognition
  • Language understanding
  • Translation

7. Why Do They Work Well?​

Advantages​

1. Brain Simulation:

  • Works like the human brain
  • A natural way of thinking

2. Flexibility:

  • Adapts to different types of data
  • Images, texts, sounds

3. Power:

  • Capable of solving complex problems
  • Excellent performance

4. Learning:

  • Learns from data
  • Improves over time

8. Challenges​

Problems​

1. Need for Data:

  • Requires a lot of data
  • To learn well

2. Computational Power:

  • Needs powerful devices
  • To process many layers

3. Time:

  • Training is slow
  • Requires patience

4. Understanding:

  • Difficult to understand how it works exactly
  • "Black box"

9. Real-Life Examples​

Where Do We Find Them?​

Image Recognition:

  • In phones
  • In security
  • In medicine

Translation:

  • Machine translation
  • Language understanding

Voice Assistants:

  • Siri, Google Assistant
  • Speech understanding

Games:

  • Smart games
  • Intelligent opponents

Conclusion​

  • Neural networks mimic the human brain
  • Consist of connected cells in layers
  • Learn from data
  • Present everywhere in our lives