What is Batch Normalization?
Batch normalization was introduced to mitigate the internal covariate shift problem in neural networks by Sergey Ioffe and Christian Szegedy in 2015. The normalization process involves calculating the mean and variance of each feature in a mini-batch and then scaling and shifting the features using these statistics. This ensures that the input to each layer remains roughly in the same distribution, regardless of changes in the distribution of earlier layers’ outputs. Consequently, Batch Normalization helps in stabilizing the training process, enabling higher learning rates and faster convergence.
What is Batch Normalization In Deep Learning?
Internal covariate shift is a major challenge encountered while training deep learning models. Batch normalization was introduced to address this issue. In this article, we are going to learn the fundamentals and need of Batch normalization. We are also going to perform batch normalization.
Table of Content
- What is Batch Normalization?
- Need for Batch Normalization
- Fundamentals of Batch Normalization
- Batch Normalization in TensorFlow
- Batch Normalization in PyTorch
- Benefits of Batch Normalization
- Conclusion