Benefits of Batch Normalization
- Faster Convergence: Batch Normalization reduces internal covariate shift, allowing for faster convergence during training.
- Higher Learning Rates: With Batch Normalization, higher learning rates can be used without the risk of divergence.
- Regularization Effect: Batch Normalization introduces a slight regularization effect that reduces the need for adding regularization techniques like dropout.
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