Deep learning

Q: How deeply does WordPress Power Toolkit: Harness AI to build next-level websites cover deep learning?

Defines multilayer neural networks and hierarchical representations, but does not explain training mechanics or network internals.

As explained in WordPress Power Toolkit: Harness AI to build next-level websites

Deep learning is a machine-learning approach that uses multilayer artificial neural networks to learn hierarchical representations from large amounts of data.

Deep learning is a subdomain of machine learning that uses artificial neural networks with multiple layers. These layers process vast amounts of data hierarchically, helping systems learn complex representations and abstractions. section 13.3

For website work, deep learning can power advanced data mining. It can uncover hidden patterns and relationships in analytics about ads, scrolling, downloads, forms, and other site components. section 12.7

AlphaGo Zero learns Go

AlphaGo Zero received only the rules of Go, without previous game data, human coaching, or expert instructions. Through deep learning algorithms, it developed unusual strategies that defeated leading human players. section 13.3.1

Deep learning connects with several major AI areas:

Machine learning provides the broader category; deep learning uses multilayer neural networks within it. section 13.3
Artificial neural networks supply the layered structure used to process information hierarchically. section 13.3
Analytics and data mining provide practical website applications for finding patterns in visitor behavior. section 12.7

How WordPress Power Toolkit: Harness AI to build next-level websites covers it

Cover of WordPress Power Toolkit: Harness AI to build next-level websites

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Also explained in

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Machine Learning for Tabular Data: XGBoost, Deep Learning, and AIA machine learning approach based on neural networks with multiple learned layers of representation.
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Inside AIA deep neural network is a neural network containing multiple hidden layers that learn hierarchical representations from simpler features.
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Data Without LabelsA deep learning model is a machine-learning model composed of multiple layers of learned representations.
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Outlier Detection in PythonDeep learning is machine learning based on neural networks with multiple computational layers that learn hierarchical representations.
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Designing Deep Learning Systems: A guide for software engineersA machine learning model that uses multiple layers of learned representations to make predictions or decisions.
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Evolutionary Deep Learning: Genetic algorithms and neural networksA deep learning model is a neural network with multiple learned layers that learns hierarchical representations from data.
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Effective Data AnalysisDeep learning is a machine-learning approach that uses multilayered neural networks to learn representations and patterns from data.
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Delivering Machine Learning Projects: From design to deploymentA deep neural network is a neural-network model composed of multiple computational layers that learn hierarchical representations from data.
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Data-Driven CybersecurityDeep learning is a machine-learning approach that uses neural networks with multiple learned layers to represent complex patterns.
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Introduction to Generative AI, Second EditionDeep learning is a machine-learning subfield based on neural networks with multiple hidden layers.

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Related concepts

machine learningkind of · Machine learning is a field of computational methods in which systems learn patterns or relationships from data to make predictions, decisions, or other outputs.
language modelincludes · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.
autoencoderincludes · An autoencoder is a neural network that encodes input data into an internal representation and decodes it to reconstruct the input or an approximation.
neural networkuses · A neural network is a computational model of interconnected, parameterized units that learns transformations from inputs to outputs.
GPT modelincludes · A GPT model is a generative pretrained Transformer language model that produces text autoregressively by predicting successive tokens from preceding context.
recurrent neural networkincludes · A recurrent neural network is a neural network that processes sequences by updating an internal state across successive inputs, allowing earlier inputs to influence later computations.

This explainer was generated by Manning's knowledge graph from the text of WordPress Power Toolkit: Harness AI to build next-level websites. The section links point to the parts of the book it draws on.