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GPT model

As explained in Machine Learning Algorithms in Depth

GPT-3 is a large autoregressive transformer language model that generates text from preceding context.

A GPT model is an autoregressive Transformer language model that generates text from preceding context. GPT-3 applies this design at large scale. section 11.5

GPT-3 helped popularize zero-shot and few-shot learning with large Transformer models. Its applications include text completion and code completion. section 11.5

Code completion with Codex

Codex, a descendant of the GPT-3 series trained on natural language and billions of lines of code, turns comments into code and completes the next line or function in context. section 11.5

How Machine Learning Algorithms in Depth covers it

How deep it goes
Defines GPT-3 as a large Transformer language model but does not detail training, architecture, or inference mechanics.
Examples
Uses text completion, code completion, and Codex turning comments into code or completing functions in context.
What you can do after
Evaluate/understand GPT-3 as a general language model supporting contextual text and code completion.
How it connects
Connects GPT-3 to Transformer models, natural language processing, zero-shot learning, few-shot learning, and code completion.

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