Fixed Seed Generators

Fixed seed generators are pseudorandom number generators (PRNGs) that use a predetermined starting value (seed) to produce a reproducible sequence of numbers. This ensures that the same seed always yields the same output sequence, making results deterministic and predictable. They are commonly used in simulations, testing, and machine learning to enable reproducibility and debugging.

Also known as: Deterministic PRNGs, Seeded Random Number Generators, Reproducible Random Generators, Fixed-Seed PRNGs, Seeded RNGs

Why learn Fixed Seed Generators?

Developers should use fixed seed generators when they need reproducible results, such as in unit testing to verify consistent behavior, in scientific simulations to compare outcomes, or in machine learning to ensure model training is repeatable. This is crucial for debugging, sharing research, and maintaining consistency across different runs or environments.

Learning Resources

• docs: Python random module documentation
• tutorial: NumPy random seeding tutorial
• course: Reproducibility in Machine Learning - Coursera course
• video: Random Number Generation Explained - YouTube video
• book: The Art of Computer Programming, Volume 2 - Book by Donald Knuth