Neural networks, often called deep learning models, are inspired by the brain. They use layers of interconnected nodes to learn complex patterns from data.
Intuition
Think of each neuron as focusing on one aspect of the problem. During training, the network adjusts weights to learn which features matter most.
This makes neural networks powerful for tasks involving non-linear patterns, such as:
- image recognition
- language understanding
- demand prediction
- autonomous driving
Inference
Inference is the stage where a trained model uses its learned knowledge to make predictions on new data.