A neural network is a type of machine learning model loosely inspired by the structure of the human brain. It’s made up of layers of interconnected nodes (like neurons) that process data and learn to recognize patterns. Neural networks are the foundation of most modern AI — including image recognition, language models, and voice assistants.
How Neural Networks Work
Data enters the input layer, flows through one or more hidden layers where patterns are learned, and exits through the output layer as a prediction or classification. During training, the network adjusts the strength of connections (weights) until it gets good at the task.
Types of Neural Networks
- Feedforward networks — the simplest type; data flows in one direction
- Convolutional Neural Networks (CNNs) — great for image processing
- Recurrent Neural Networks (RNNs) — designed for sequential data like text or time series
- Transformers — the architecture behind ChatGPT and modern LLMs
Real-World Applications
- Recognizing faces in photos
- Translating languages in real time
- Generating text (ChatGPT, Claude)
- Detecting fraud in transactions
- Self-driving car systems
You don’t need to understand neural networks deeply to use AI tools, but knowing the basics helps you understand what these tools can and can’t do.
Related: Deep Learning
Related: Machine Learning
