📊 Batch Normalization
Stabilize and accelerate deep neural network training
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Pooling & Stride Playground
Training Stability Revolution
Batch Normalization (BatchNorm) is one of the most impactful innovations in deep learning. Introduced in 2015, it transformed how we train deep networks by normalizing layer inputs during training, leading to faster convergence and better performance.
🎯 Why BatchNorm Matters
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Faster training: Enables higher learning rates
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Stability: Reduces sensitivity to initialization
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Regularization: Acts as mild regularizer
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Deeper networks: Makes very deep models trainable
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Without BatchNorm
Training is sensitive and unstable
✗Vanishing/exploding gradients
✗Slow convergence
✗Careful initialization required
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With BatchNorm
Training is stable and efficient
✓Stable gradient flow
✓Fast convergence
✓Less sensitive to hyperparameters
📈 Impact on Modern AI
ResNet (2015)
Enabled 152-layer networks
Inception v2/v3
Improved training speed 14x
Modern CNNs
Standard component today