What is batch normalization?

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Batch Normalization (BN) is a deep learning technique used to make neural networks train faster and more stable. It works by normalizing the inputs of each layer so that they have a consistent distribution (mean and variance), reducing issues caused by internal changes in data distribution during training (called internal covariate shift).

🔹 How it works:

1. For each mini-batch during training, BN computes the mean and variance of the inputs.

2. It normalizes the inputs so they have zero mean and unit variance.

3. To keep the network flexible, BN adds two learnable parameters:

   • γ (gamma): scales the normalized values.

   • β (beta): shifts the normalized values.
     This way, the network can still learn the best distribution if needed.

🔹 Why it’s useful:

• ⚡ Faster training: Reduces the problem of vanishing/exploding gradients.

• 🎯 Stability: Keeps input distributions consistent across layers.

• 🔄 Higher learning rates possible: Because training is more stable.

• 🛡️ Regularization effect: Slightly reduces overfitting by adding noise from mini-batch statistics.

🔹 Example use case:

In an image classification CNN, without BN, early layers might shift activation distributions too much, slowing convergence. With BN, each batch is normalized, helping the model converge quickly and achieve higher accuracy.

👉 In short, Batch Normalization improves speed, stability, and performance of neural networks by normalizing activations at each layer during training.

Read more:

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