Need for Batch Normalization
Batch Normalization is extension of concept of normalization from just the input layer to the activations of each hidden layer throughout the neural network. By normalizing the activations of each layer, Batch Normalization helps to alleviate the internal covariate shift problem, which can hinder the convergence of the network during training.
In traditional neural networks, as the input data propagates through the network, the distribution of each layer’s inputs changes. This phenomenon, known as internal covariate shift, can slow down the training process. Batch Normalization aims to mitigate this issue by normalizing the inputs of each layer.
The inputs to each hidden layer are the activations from the previous layer. If these activations are normalized, it ensures that the network is consistently presented with inputs that have a similar distribution, regardless of the training stage. This stability in the distribution of inputs allows for smoother and more efficient training.
By applying Batch Normalization into the hidden layers of the network, the gradients propagated during backpropagation are less likely to vanish or explode, leading to more stable training dynamics. This ultimately facilitates faster convergence and better performance of the neural network on the given task.
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
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