Batch Normalization
Batch Normalization is a technique that normalizes the inputs of each layer within a mini-batch during training, stabilizing the learning process and accelerating convergence.
Frequently Asked Questions
How does Batch Normalization improve training speed?▼
It reduces internal covariate shift (the change in the distribution of network activations during training), allowing higher learning rates and reducing dependency on initialization.
What is the difference between Batch Normalization and Layer Normalization?▼
Batch Normalization normalizes across the batch dimension for each feature individually, while Layer Normalization normalizes across the features for each individual data point in the batch.
Quick Facts
- CategoryNeural Architectures
- Key ApplicationConvolutional neural network training, feedforward layer setups, and deep network optimization.
Coverage Trend12 Weeks
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