Normalization layer

Q: How deeply does Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI cover normalization layer?

Explains the range problem and gives an adaptation-based Keras implementation, but stops short of mathematical derivation.

As explained in Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI

A normalization layer is a neural-network layer that transforms input feature values to a standardized numeric distribution during model computation.

A normalization layer standardizes continuous feature values during model computation, typically producing values centered around 0 with a standard deviation of 1. This prevents large-range features from dominating training: in the Kuala Lumpur property data, Bathrooms ranges from 0 to 20 while Size reaches 11 million. section 9.4.3

In a Keras tabular model, create the layer from training data, adapt it to one continuous column, and apply it to that model input. Use the resulting normalized values alongside separately encoded categorical features. section 9.3.2

Kuala Lumpur property prediction

For Bathrooms, Car Parks, Rooms, and Size, build one normalization layer per continuous column using the training dataset. Keep Location, Property Type, Furnishing, and Size_type_bin on a categorical encoding path before concatenating all features for the neural network.

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Authorization in ActionA normalization layer is an architectural component that collects, standardizes, validates, and assembles data from heterogeneous sources for downstream processing.

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Related concepts

schemauses · A data schema is a formal specification of the structure, fields, types, relationships, and constraints that data must or may satisfy.
transformer blockpart of · A transformer block is a neural-network module that transforms sequence representations using attention and feed-forward computation.
standardizationimplements · Standardization is the transformation of numerical data by centering and scaling it to a common reference, typically to a mean of zero and a standard deviation of one.
batch normalizationincludes · Batch normalization is a neural-network technique that normalizes activations using statistics computed over a mini-batch, with learned scale and shift parameters and typically running statistics for inference.
layer normalizationincludes · Layer normalization is a neural-network operation that normalizes activations across specified feature dimensions independently for each example, often applying learned scale and bias parameters.
entityproduces · An entity is a distinct, identifiable person, object, event, or concept represented as a unit in a data, software, or semantic model.

This explainer was generated by Manning's knowledge graph from the text of Machine Learning for Tabular Data: XGBoost, Deep Learning, and AI. The section links point to the parts of the book it draws on.