What is Batch Normalization?
Batch normalization is a technique used in deep learning to improve the training of neural networks. It works by normalizing the inputs of each layer, ensuring that they have a mean of zero and a standard deviation of one. This process helps to stabilize the learning process, making it faster and more efficient.
Purpose
The primary purpose of batch normalization is to reduce internal covariate shift, which refers to the changes in the distribution of network activations due to the updates of the weights. By normalizing the inputs, batch normalization allows for higher learning rates and a reduction in sensitivity to initialization.
How it Works
Batch normalization operates on mini-batches of data during training. It calculates the mean and variance of the mini-batch, then normalizes the inputs. The layer also introduces two learnable parameters, gamma and beta, which allow the model to scale and shift the normalized output.
Benefits
- Accelerates training speed.
- Improves model stability.
- Reduces the risk of overfitting.
Conclusion
Overall, batch normalization is a significant innovation in deep learning that enhances the training process, leading to better-performing models across various applications.