What is Artificial Neural Networks
Machine learning models inspired by a network of interconnected nodes that learn to transform data into the desired output.
Definition
An artificial neural network consists of layers and parameters that change during training. It can recognize images, process speech, generate text, translate languages, predict events and find complex patterns. Modern large models are also based on neural network architectures.
Example
An image recognition neural network takes pixels from a photo and outputs the probability that it is a cat, a car, or a person.
Why it matters
The term is important for understanding modern AI: many popular services are built on neural networks, even if the user only sees a simple interface.
How it works
The data passes through the layers of the network, each layer extracts and transforms features, and training adjusts the weights so that the error becomes smaller.
Where it is used
- image recognition
- language models
- forecasting and generation
Limitations
Neural networks require data, computation, and verification. They may be difficult to explain and make mistakes with examples that differ from the ones taught.