juniorDeep Learning
What is an Artificial Neural Network (ANN)?
Updated May 16, 2026
Short answer
An Artificial Neural Network is a computational model inspired by biological neurons that learns patterns through interconnected layers of nodes.
Deep explanation
An ANN consists of input layers, hidden layers, and output layers. Each neuron receives weighted inputs, applies a transformation, and passes the output through an activation function. During training, the network adjusts weights using optimization algorithms like gradient descent to minimize prediction error. Hidden layers enable the network to learn increasingly abstract representations. ANNs are the foundational architecture behind modern Deep Learning systems.
Real-world example
ANNs are used in customer churn prediction, fraud detection, and stock price forecasting.
Common mistakes
- Believing more layers always improve performance regardless of dataset size or architecture quality.
Follow-up questions
- What is a neuron in ANN?
- Why are hidden layers important?
- What is the role of weights?