Offline learning
term_id: offline_learning
Category: training_techniques
Definition
Also known as batch learning, offline learning involves training machine learning models on a fixed dataset collected previously. Unlike online learning, the model does not update its parameters in real-time as new data arrives. This approach is computationally efficient for large-scale training but requires periodic retraining to incorporate new information, making it suitable for scenarios where immediate adaptation is not critical.
Summary
Offline learning is a training paradigm where models are trained on static datasets without interacting with the live environment during the learning phase.
Key Concepts
- Batch Training
- Static Datasets
- Model Retraining
- Computational Efficiency
- Historical Data
Use Cases
- Training recommendation systems on historical user data
- Building fraud detection models from past transactions
- Developing image classifiers for archival photos