Neural Architecture Search

Q: How deeply does Evolutionary Deep Learning: Genetic algorithms and neural networks cover Neural Architecture Search?

Implements representation, operators, training-based fitness, failure recovery, and five-generation results.

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

Architecture search is the systematic exploration of alternative neural-network structures to identify one that performs well on a specified task.

Neural architecture search systematically evolves alternative convolutional-network structures, using genetic algorithms to explore models that perform well on a task. section 7.5

A practical evolutionary search loop:

Represent each candidate network as a gene sequence stored in a list, allowing individuals to vary in length. section 7.5

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

Cover of Evolutionary Deep Learning: Genetic algorithms and neural networks

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Related concepts

AutoMLalternative to · Automated machine learning (AutoML) uses computational procedures to automate selected tasks in developing and applying machine-learning models, such as data preparation, model selection, training, evaluation, and tuning.
model architectureuses · The structural organization of a computational system’s components, relationships, and operations that determines how it processes information or behaves.
reinforcement learninguses · Reinforcement learning is a machine-learning paradigm in which an agent learns actions or a policy through interaction with an environment and evaluative feedback, such as rewards.
evolutionary computationuses · Evolutionary computation is a family of search and optimization methods that iteratively improve candidate solutions through variation and selection inspired by biological evolution.
gradient descentuses · Gradient descent is an iterative optimization method that updates parameters opposite the gradient of an objective function to reduce its value.

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.