Training Techniques

Curriculum Distillation is a technique in AI that simplifies training by organizing tasks from easy to difficult.

Gradient accumulation is a technique that allows training deep learning models with larger effective batch sizes.

Gradient clipping is a technique used to prevent exploding gradients during neural network training.

Hint Training is a method where AI models learn from specific guidance or cues to improve performance on tasks.

Internal covariate shift refers to changes in the distribution of network inputs during training.

Learning Rate Warmup gradually increases the learning rate at the beginning of training to improve model convergence.

A One-Cycle Policy is an AI training approach that optimizes learning by updating parameters in a single cycle for each data batch.

Scheduled Sampling is a technique in machine learning that adjusts training data over time to improve model performance.