Learning Curve
Machine Learning
A technique that normalizes the inputs across the features in a layer to stabilize and accelerate neural network training. Unlike batch normalization it operates on a single training example.
Detailed Explanation
Layer normalization is a technique used in neural networks to normalize the inputs across all features within a single training example. By adjusting and scaling these inputs, it stabilizes and accelerates training, especially in recurrent and transformer models, without relying on batch statistics. This ensures more consistent learning and better convergence, making models more robust and efficient.
Use Cases
- •Layer normalization speeds up training in RNNs and Transformers by stabilizing feature inputs, leading to faster convergence and improved model robustness.
Related Terms
Other terms in the Machine Learning category