Neural Network
A neural network is a system inspired by the human brain that processes data through connected layers of nodes.
It is used to recognize patterns and make decisions.
Why neural networks are important
Neural networks allow computers to:
- Recognize images
- Understand speech
- Translate languages
- Detect patterns in data
They are the foundation of many modern AI systems.
How it works
A neural network consists of layers:
- Input layer – receives data
- Hidden layers – process information
- Output layer – produces the result
Each node (neuron) processes data and passes it to the next layer.
How learning happens
Neural networks learn by adjusting weights between nodes.
- Correct predictions → strengthen connections
- Incorrect predictions → adjust connections
This process is called training.
Real-world examples
Neural networks are used in:
- Image recognition (faces, objects)
- Voice assistants
- Self-driving cars
- Medical diagnosis
Neural Network vs Brain
Neural networks are inspired by the human brain, but they are much simpler.
They simulate how neurons connect and process information.
Why learning neural networks matters
Understanding neural networks helps you:
- Understand modern AI systems
- Work with deep learning
- Build intelligent applications
They are a core part of machine learning.
A simple example
Think of a neural network like a team of decision-makers.
Each one analyzes part of the information, and together they produce a final answer.
Related terms
- What is Deep Learning?
- What is Model?
- What is Training Data?
Source
Information simplified from the Wikipedia article “Artificial Neural Network”.