What Batch Normalization Meaning, Applications & Example
Technique to standardize the inputs to neural network layers for faster training.
What is Batch Normalization?
Batch Normalization is a technique used in neural networks to standardize the inputs to a layer by adjusting and scaling the activations. It helps to stabilize and speed up training, allowing the network to use higher learning rates, and can improve generalization by reducing internal covariate shift.
How Batch Normalization Works
- Normalization: Each mini-batch ’s inputs are normalized by subtracting the batch mean and dividing by the batch standard deviation.
- Scaling and Shifting: The normalized values are scaled and shifted by learnable parameters, allowing the network to maintain representational capacity.
Applications of Batch Normalization
- Image Classification: Used in CNNs to improve convergence speed and performance.
- Recurrent Neural Networks (RNNs): Helps stabilize training in sequential data tasks, such as language modeling.
- GANs (Generative Adversarial Networks): Enables smoother training and higher-quality generated outputs.
Example of Batch Normalization
In image recognition models, batch normalization helps prevent vanishing or exploding gradients, allowing deeper networks like ResNet to train efficiently and achieve higher accuracy.