Neural Network
A neural network is a machine-learning model loosely inspired by the brain, made of layered interconnected nodes that learn patterns from data. Neural networks underpin most modern AI, including the deep-learning systems used for vision inspection, anomaly detection and forecasting in industry.
By adjusting the strengths of connections between nodes during training, a neural network learns to map inputs (images, sensor signals, text) to outputs (a defect class, a fault, a forecast). Deep networks with many layers excel at complex patterns, powering computer-vision quality inspection, vibration-based fault detection and demand or failure forecasting — though they need substantial, good-quality data to train well.
Related reading
- Cognex Deep Learning (VisionPro)
- Landing AI (LandingLens)
- Machine Learning (Industrial)
- AI Vision Inspection (Machine Vision QC)
- Anomaly Detection
Related terms
Machine Learning (Industrial) · AI Vision Inspection (Machine Vision QC) · Anomaly Detection · Time-Series Forecasting · Model Drift · Unsupervised Learning