What is Batch Normalization?
Batch normalization is a deep learning technique aimed at improving the training of neural networks. It was introduced by Sergey Ioffe and Christian Szegedy in 2015. The primary purpose of batch normalization is to address the issue of internal covariate shift, which occurs when the distribution of inputs to a layer changes during training, making it harder for the network to converge.
How It Works
Batch normalization operates by normalizing the input of each layer so that they have a mean of zero and a variance of one. This is done by calculating the mean and variance of the inputs over a mini-batch and subsequently adjusting the inputs to achieve standardization. Additionally, batch normalization introduces two learnable parameters, scale (gamma) and shift (beta), which allow the network to maintain the ability to learn complex representations.
Benefits
- Speeds up training: By reducing internal covariate shift, batch normalization allows for higher learning rates.
- Improves stability: It can mitigate issues related to vanishing or exploding gradients.
- Acts as a regularizer: Batch normalization can reduce the need for other forms of regularization, such as dropout.
Conclusion
In summary, batch normalization is a pivotal technique in deep learning that enhances the training process by normalizing layer inputs, ultimately leading to faster convergence and improved model performance.