Sequence generation
Sequence generation is the process of producing an ordered sequence of tokens, characters, or other elements, typically conditioned on input or prior elements.
Deep Learning with R, Third Edition
Sequence generation is the production of ordered data elements by a computational model.
- How it is used
- Frames ordered-data production across music, handwriting, and language-modeling applications.
- How deep it goes
- Traces major historical milestones but stops short of implementation details, algorithms, and mathematical derivations.
- Examples
- Uses music generation, handwriting from pen-position time series, and next-token text generation as concrete examples.
- What you can do after
- Evaluate sequence-generation applications and recognize next-token prediction as a language-modeling strategy.
Read how Deep Learning with R, Third Edition explains sequence generation →
Deep Learning with Python, Third Edition
The task of producing an ordered sequence of tokens, characters, or other elements from a model rather than a single label.
- How it is used
- Frames model-driven production of ordered data, especially text, music, and handwriting.
- How deep it goes
- Defines next-token prediction and surveys milestones, but stops short of implementation details or decoding procedures.
- Examples
- Uses music, handwriting trajectories, Shakespeare text generation, and GPT language pretraining as examples.
- What you can do after
- Evaluate how sequence-generation ideas extend from next-token prediction to generated music, handwriting, and text.
Read how Deep Learning with Python, Third Edition explains sequence generation →
Related concepts
- sequence predictionincludes · Sequence prediction is the task of predicting or generating elements of an ordered sequence from preceding elements or contextual input.
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