gradient-free-optimization
CommunityOptimize complex ML problems with gradient-free methods.
Authorhung-phan
Version1.0.0
Installs0
System Documentation
What problem does it solve?
This Skill addresses the challenge of optimizing complex machine learning problems where traditional gradient-based methods are not applicable or effective.
Core Features & Use Cases
- Gradient-Free Optimization: Offers a suite of algorithms for non-differentiable or noisy objectives.
- Use Cases: Ideal for hyperparameter optimization, neural architecture search, prompt optimization, reinforcement learning, and combinatorial problems.
- Example: Optimize the hyperparameters of a machine learning model without relying on gradient information.
Quick Start
Use the gradient-free-optimization skill to optimize the learning rate of your model.
Dependency Matrix
Required Modules
cmapyswarmsoptunascikit-optimize
Components
scriptsreferences
💻 Claude Code Installation
Recommended: Let Claude install automatically. Simply copy and paste the text below to Claude Code.
Please help me install this Skill: Name: gradient-free-optimization Download link: https://github.com/hung-phan/ml-skills/archive/main.zip#gradient-free-optimization Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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