Definition
Curriculum learning mimics human education by presenting training data in a structured order, typically starting with simple samples and gradually increasing complexity. This approach helps neural networks converge faster, avoid local minima, and achieve better generalization performance compared to random data shuffling. It requires defining a meaningful difficulty metric for the dataset to sequence samples effectively during the training process.
Summary
A training strategy where models learn from easy examples first before progressing to harder ones.
Key Concepts
- Progressive Difficulty
- Sample Sequencing
- Convergence Speed
- Generalization
Use Cases
- Training deep neural networks on complex image datasets
- Language modeling with varying sentence complexities
- Reinforcement learning tasks with sparse rewards