Language model

As explained in Deep Learning with R, Third Edition

A large language model is a machine-learning model trained on large text corpora to understand and generate natural-language text.

How Deep Learning with R, Third Edition covers it

How deep it goes

Defines the next-token probability distribution and explains the scale problem; introduces a character-level build without detailing its implementation here.

Examples

Uses a 20,000-word vocabulary calculation and a next-character model for Shakespeare-like text.

What you can do after

Build it from scratch as a small next-character model for generating Shakespeare-like text.

How it connects

Connects next-token prediction with probability distributions, sequence classification, and text generation.

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Also explained in

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  The Complete Obsolete Guide to Generative AIA machine-learning model trained on large text corpora to process and generate natural-language text.
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Compare how 67 books explain language model →

Related concepts

• 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.
• promptused by · An input, typically containing instructions or context, supplied to an artificial-intelligence model to guide or elicit its output.
• AI agentused by · An AI agent is a computational system that uses artificial intelligence to interpret information, select actions, and pursue goals through interaction with an environment.
• generationproduces · Generation is the computational production of outputs from supplied inputs or context, often by applying a model to produce predictions or new content.
• ChatGPTincludes · ChatGPT is OpenAI’s conversational AI service, which lets users interact with generative AI models and related tools through natural-language prompts.
• 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.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.