Overview of Batch Normalization
Batch normalization is a technique to improve the training of deep neural networks by stabilizing and accelerating the learning process. Introduced by Sergey Ioffe and Christian Szegedy in 2015, it addresses the issue known as “internal covariate shift” where the distribution of each layer’s inputs changes during training, as the parameters of the previous layers change.
What is Batch Normalization in CNN?
Batch Normalization is a technique used to improve the training and performance of neural networks, particularly CNNs. The article aims to provide an overview of batch normalization in CNNs along with the implementation in PyTorch and TensorFlow.
Table of Content
- Overview of Batch Normalization
- Need for Batch Normalization in CNN model
- How Does Batch Normalization Work in CNN?
- 1. Normalization within Mini-Batch
- 2. Scaling and Shifting
- 3. Learnable Parameters
- 4. Applying Batch Normalization
- 5. Training and Inference
- Applying Batch Normalization in CNN model using TensorFlow
- Applying Batch Normalization in CNN model using PyTorch
- Advantages of Batch Normalization in CNN