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How Neural Networks Work?

What Is a Neural Network?

A Neural Network is a computer system that is designed to work a little like the human brain. Just like our brain uses neurons to think, learn, and remember things, a neural network uses artificial neurons (also called nodes) to do similar tasks.

Imagine a network of lights where each light bulb (neuron) is connected to many others. Some bulbs turn on when certain conditions are met, and their light helps turn other bulbs on. This chain reaction is how the network processes information.

The Basic Structure of a Neural Network

A typical neural network has three main layers:

  1. Input Layer: This is where the information goes in. For example, if you are feeding a picture of a cat, each pixel (tiny square in the image) becomes an input.
  2. Hidden Layers: These are layers in the middle where the real "thinking" happens. Each neuron in these layers takes inputs, does some calculations, and passes the result to the next layer.
  3. Output Layer: This gives the final result. For example, "Yes, this is a cat" or "No, this is not a cat".

How Does a Neural Network Learn?

Neural networks learn by example—just like humans do.

Imagine this: You are teaching a child to recognise apples and oranges. You show them a red round fruit and say “This is an apple.” You repeat this several times with different apples and oranges. Slowly, the child starts noticing patterns—apples are round, red or green, and have a certain shape.

In the same way, a neural network is trained using lots of examples. If you want it to recognise apples and oranges:

  1. You feed it thousands of images labeled as “apple” or “orange”.
  2. The network finds patterns in the pictures (color, shape, texture).
  3. Over time, it gets better at telling the difference.

This process is called training the neural network.

A Simple Example: Predicting House Prices

Let’s say we want to build a neural network that predicts house prices.

Input:

  1. Size of the house (in square feet)
  2. Number of bedrooms
  3. Location score (how good the area is)

Output:

  1. Estimated price of the house

The network will:

  1. Take the inputs (size, bedrooms, location),
  2. Process them in hidden layers (where it learns patterns, like “bigger houses cost more”),
  3. Output a number (estimated price).

The more examples (data) we give it, the better it learns to predict accurate prices.

What Happens Inside a Neuron?

Here’s a very simplified version:

  1. A neuron receives some numbers (inputs).
  2. It multiplies each number by a weight (importance).
  3. It adds them up and applies a rule (called an activation function) to decide if it should pass the message forward.

This is similar to:

  1. You weigh options before making a decision.
  2. For example, when buying a phone, you might consider price, camera, and battery life. You give more importance (weight) to what matters to you and make a decision.

What If It Makes a Mistake?

Neural networks don’t get everything right the first time. When the output is wrong (for example, it says an apple is an orange), the network learns from the mistake. This process is called backpropagation.

It adjusts the weights slightly so that it can do better next time. Over thousands of rounds, the network becomes very accurate.

Where Are Neural Networks Used?

Neural networks are used in many everyday things:

  1. Face recognition on phones
  2. Voice assistants like Siri or Alexa
  3. Spam filters in your email
  4. Self-driving cars
  5. Language translation
  6. Medical diagnosis

Summary

Let’s sum it up simply:

  1. Neural networks are systems that learn from data, inspired by the human brain.
  2. They consist of layers of neurons that pass messages.
  3. They improve by learning from examples and mistakes.
  4. They are used in many smart technologies we use every day.

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