What is Model Training?
Definition
Model training is the process of teaching a machine learning model by exposing it to data and adjusting its internal parameters (weights) to minimize prediction errors. Through iterative optimization, the model learns to recognize patterns and make accurate predictions on new, unseen data.
The Training Process
- Initialize: Set initial random weights
- Forward Pass: Process input data through the model
- Calculate Loss: Measure prediction error
- Backpropagation: Calculate gradient of loss
- Update Weights: Adjust parameters to reduce error
- Repeat: Iterate until convergence