Understanding Batch Normalization: Making Training Better

Batch Normalization, or BN for short, is a technique that helps make models stronger when they're learning. Let’s break it down into simpler parts:

1. Stabilizing Learning:

   • BN helps keep the input to each layer uniform.
   • It makes sure the average (mean) is 0 and the spread (variance) is 1.
   • This helps the learning process stay steady and speeds things up.
   • When data changes (called internal covariate shift), BN helps smooth out these changes.

2. Helpful Noise:

   • Batch Normalization adds a bit of randomness while training.
   • Each mini-batch of data is adjusted slightly, which helps keep things fresh.
   • This little noise can help prevent overfitting, which is when a model learns too much from the training data and doesn't do well on new data.
   • For example, in a type of model called a convolutional neural network (CNN), BN helps it understand the main patterns rather than just the random stuff in the training data.

3. Real-World Examples:

   • In real life, models that use BN often do better than those that only use a method called Dropout.
   • Take ResNet models, for instance; they include BN layers and usually show better accuracy on challenging datasets like ImageNet.

In conclusion, while Batch Normalization doesn’t replace other methods like Dropout, it works well alongside them. This combination helps make models stronger during training and improves their performance when they’re used in the real world.