ParameterGrid

Q: How deeply does Evolutionary Deep Learning: Genetic algorithms and neural networks cover ParameterGrid?

Explains dictionary-to-combination construction and iteration, while deferring model evaluation and search-result analysis.

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

ParameterGrid is a scikit-learn helper class that constructs the Cartesian product of parameter names and lists of candidate values.

ParameterGrid is a scikit-learn helper that turns a dictionary of parameter names and candidate-value lists into every Cartesian combination. It supports grids with any number of dimensions, making it useful for systematic hyperparameter optimization. section 5.3.1

A four-parameter grid

A grid containing middle-layer sizes, learning rates, batch sizes, and epoch counts produces 90 combinations. Iteration returns successive configurations, such as middle_layer=20 followed by middle_layer=26, while fixed single-value parameters remain unchanged.

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

Cover of Evolutionary Deep Learning: Genetic algorithms and neural networks

liveBook manning.com

Read Evolutionary Deep Learning: Genetic algorithms and neural networks on liveBook Get the book at manning.com

Related concepts

scikit-learnuses · scikit-learn is an open-source Python library providing tools for classical machine learning, including algorithms, preprocessing, model selection, and evaluation.
hyperparameter gridimplements · A hyperparameter grid is a structured set of candidate hyperparameter values whose combinations define configurations for systematic evaluation.

This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.