Prior knowledge
Prior knowledge is information, beliefs, skills, or assumptions available to an agent before it encounters a task or incorporates new observations.
Deep Learning with R, Third Edition
Prior knowledge is information or structural assumptions available to an agent before it learns from or encounters the current problem.
- How it is used
- Frames architectural choices as assumptions that help a model learn from data and frames prior knowledge as information available for novel-task adaptation.
- How deep it goes
- Explains priors through layer choices and a novel-task evaluation setup, without giving a general procedure for discovering the right priors.
- Examples
- Uses a linear classifier, image convolutions, and an ARC-AGI input–output grid task.
- What you can do after
- Evaluate/understand it by identifying assumptions in an architecture and distinguishing trained task skill from adaptation to unfamiliar tasks.
Read how Deep Learning with R, Third Edition explains prior knowledge →
Bayesian Optimization in Action
Prior knowledge is information or assumptions about an objective function that are available before observing all relevant data.
- How it is used
- Guides Gaussian-process prediction and Bayesian optimization when objective evaluations are expensive.
- How deep it goes
- Defines prior knowledge through mean and covariance functions, then details smoothness, scales, ARD, and hyperparameter optimization.
- Examples
- Uses weather forecasting, hyperparameter-tuning accuracy, and housing-price assumptions about bounds, positivity, trends, and feature sensitivity.
- What you can do after
- Apply it via GPyTorch by selecting mean and covariance functions and setting their parameters for better surrogate-guided optimization.
Read how Bayesian Optimization in Action explains prior knowledge →
Related concepts
- covariance functionuses · A covariance function, also called a kernel, specifies the covariance between function values at pairs of inputs in a Gaussian process.
- mean functionuses · A mean function maps an input to the expected value of a random function before conditioning on observed data.
- problem solvingused by · Problem solving is the process of analyzing a problem and developing, selecting, or applying a solution to address it.
These guides are generated from the text of Manning books by Manning's knowledge graph and link to the sections they draw on. The full books are available on liveBook and at manning.com.