batch normalization layers
Batch normalization layers are a technique used in deep learning that normalizes the input across a batch of training examples to accelerate training and improve model performance. It helps in reducing the internal covariate shift and allows for faster convergence during training by efficiently normalizing the intermediate outputs of layers.
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Similar Concepts
- attention layers
- autoencoder layers
- batch gradient descent
- batch normalization
- boundary layers
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- convolutional neural networks
- convolutional neural networks (cnn)
- dropout layers
- embedding layers
- fully connected layers
- multilayer perceptrons
- pooling layers
- recurrent layers
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