mlrose
stable
  • Overview
  • Tutorial - Getting Started
  • Tutorial - Travelling Saleperson Problems
  • Tutorial - Machine Learning Weight Optimization Problems
  • Algorithms
  • Decay Schedules
  • Optimization Problem Types
  • Fitness Functions
  • Machine Learning Weight Optimization
mlrose
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  • mlrose: Machine Learning, Randomized Optimization and SEarch
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mlrose: Machine Learning, Randomized Optimization and SEarch¶

mlrose is a Python package for applying some of the most common randomized optimization and search algorithms to a range of different optimization problems, over both discrete- and continuous-valued parameter spaces.

The source code was written by Genevieve Hayes and is available on GitHub.

User Guide¶

  • Overview
    • Project Background
    • Main Features
    • Installation
    • Licensing, Authors, Acknowledgements
  • Tutorial - Getting Started
    • What is an Optimization Problem?
    • Why use Randomized Optimization?
    • Solving Optimization Problems with mlrose
    • Define a Fitness Function Object
    • Define an Optimization Problem Object
    • Select and Run a Randomized Optimization Algorithm
    • Summary
    • References
  • Tutorial - Travelling Saleperson Problems
    • What is a Travelling Salesperson Problem?
    • Solving TSPs with mlrose
    • Define a Fitness Function Object
    • Define an Optimization Problem Object
    • Select and Run a Randomized Optimization Algorithm
    • Summary
  • Tutorial - Machine Learning Weight Optimization Problems
    • What is a Machine Learning Weight Optimization Problem?
    • Solving Machine Learning Weight Optimization Problems with mlrose
    • Data Pre-Processing
    • Neural Networks
    • Linear and Logistic Regression Models
    • Summary

API Reference¶

  • Algorithms
  • Decay Schedules
  • Optimization Problem Types
  • Fitness Functions
  • Machine Learning Weight Optimization
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© Copyright 2019, Genevieve Hayes Revision 2a9d604e.

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