How deeply does Distributed Machine Learning Patterns cover batch normalization?

Defines normalization targets and layer placement, but stops short of detailing batch-statistics mathematics or inference behavior.

Batch normalization

As explained in Distributed Machine Learning Patterns

Batch normalization is a neural-network technique that normalizes intermediate activations using batch statistics during training.

Batch normalization is a neural-network layer that normalizes pre-activations toward zero mean and unit standard deviation for each neuron in a layer. It appears as a CNN variant alongside basic and dropout models. section 9.2.1

A practical implementation places batch normalization between convolutional processing and subsequent activation or pooling:

Construct a sequential CNN with convolutional, pooling, flattening, and dense layers. section 9.2.1
Train the batch-normalized model as one candidate in parallel with basic CNN and dropout variants. section 9.4.1

How Distributed Machine Learning Patterns covers it

Cover of Distributed Machine Learning Patterns

liveBook manning.com

Read Distributed Machine Learning Patterns on liveBook Get the book at manning.com

Also explained in

liveBook manning.com

Inside Deep Learning: Math, Algorithms, ModelsA normalization technique that standardizes activations using statistics computed across a batch.
liveBook manning.com

Deep Learning with PyTorch, Second EditionA neural-network layer or operation that normalizes activations using batch statistics and behaves differently in training versus evaluation.
liveBook manning.com

Deep Learning with R, Third EditionBatch normalization is a neural-network normalization technique that standardizes layer activations using mini-batch statistics during training and stored moving statistics during inference.
liveBook manning.com

Sutskever's ListBatch normalization is a normalization technique that standardizes intermediate activations using statistics computed from training minibatches.
liveBook manning.com

Deep Learning with Python, Third EditionA normalization technique used in neural networks that stabilizes activations during training, commonly implemented as a normalization layer.
liveBook manning.com

Learn Generative AI with PyTorchA normalization technique that standardizes activations within minibatches to stabilize and speed neural-network training.
liveBook manning.com

Deep Learning with JAXA neural-network normalization layer that normalizes activations using batch statistics during training and stored running statistics during inference.
liveBook manning.com

Evolutionary Deep Learning: Genetic algorithms and neural networksBatch normalization is a neural-network technique that normalizes intermediate activations using statistics computed over training batches.

Compare how 9 books explain batch normalization →

Related concepts

layer normalizationcontrasts with · Layer normalization standardizes neural-network activations across specified feature dimensions for each example independently, often applying learned scale and bias parameters.
local response normalizationalternative to · Local response normalization scales an activation relative to neighboring activations at the same spatial location across adjacent feature maps.
standard deviationuses · Standard deviation is the nonnegative square root of variance and measures the typical dispersion of values around their mean in the original units.
exponential moving averageuses · An exponential moving average is a recursively updated weighted average that gives greater influence to recent observations while exponentially decreasing the influence of older ones.

This explainer was generated by Manning's knowledge graph from the text of Distributed Machine Learning Patterns. The section links point to the parts of the book it draws on.