How deeply does Grokking Machine Learning cover generator?

Defines the generator’s role and training interaction but does not detail its internal architecture or optimization.

Generator

As explained in Grokking Machine Learning

A generator is the component of a generative adversarial network that produces synthetic data intended to resemble real data.

A generator is the part of a generative adversarial network (GAN) that creates synthetic data designed to look real. In image generation, it produces fake images while a discriminator tries to distinguish them from real images. section 10.5

Generating realistic faces

During training, real images go to the discriminator alongside images produced by the generator. For a dataset of human faces, this competition can produce faces that look real enough to fool people. section 10.5

How Grokking Machine Learning covers it

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Also explained in

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Learn Generative AI with PyTorchThe GAN component that maps latent inputs to synthetic samples intended to look like training data.
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Inside Deep Learning: Math, Algorithms, ModelsIn a GAN, the neural network that maps latent inputs to synthetic examples.
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Evolutionary Deep Learning: Genetic algorithms and neural networksA GAN generator is the neural-network component of a generative adversarial network that maps latent inputs or noise samples to synthetic data.
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Functional Programming in Scala, Second EditionA generator is a parameterized representation of a computation that produces test-case values, potentially for any element type.
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Generative AI in ActionA text generator is a model or model component that produces natural-language text from input context.
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Build a Text-to-Image Generator (from Scratch)A GAN generator is a neural network that transforms random input noise into synthetic samples intended to resemble real data.
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Outlier Detection in PythonA generator is the component of a generative adversarial network that produces synthetic outputs from input noise or latent representations.
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Transformers in ActionA generator is a generative model component that produces an output from an input representation or retrieved context.
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Introduction to Generative AI, Second EditionA generator is the component of a generative adversarial network that produces synthetic data intended to resemble authentic data.
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Data Without LabelsA generator is the component of a generative adversarial network that maps input noise or signals to synthetic data.
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Building Reliable AI SystemsA model component that produces a response from a query and supplied context.

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Related concepts

generative adversarial networkpart of · A generative adversarial network is a generative model with a generator and discriminator trained in competition, enabling the generator to produce samples resembling a target dataset or distribution.
retrieval-augmented generationused by · Retrieval-augmented generation is a technique that retrieves relevant information from external sources and supplies it as context to a generative model when producing an output.
latent spaceuses · A latent space is a learned representational space in which numerical coordinates encode information or features inferred from observed data.
noiseuses · Noise is variation, content, or data treated as unwanted, irrelevant, or distinct from the signal, pattern, or structure of interest.
language modelkind of · A language model is a computational model that represents patterns in linguistic tokens to estimate sequence probabilities and predict or generate language from context.

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