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optimizing-deep-learning-models

17.1

This skill optimizes deep learning models using various techniques. It is triggered when the user requests improvements to model performance, such as increasing accuracy, reducing training time, or minimizing resource consumption. The skill leverages advanced optimization algorithms like Adam, SGD, and learning rate scheduling. It analyzes the existing model architecture, training data, and performance metrics to identify areas for enhancement. The skill then automatically applies appropriate optimization strategies and generates optimized code. Use this skill when the user mentions "optimize deep learning model", "improve model accuracy", "reduce training time", or "optimize learning rate".

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Third-Party Agent Skill: Review the code before installing. Agent skills execute in your AI assistant's environment and can access your files. Learn more about security

Installation for Agentic Skill

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skilz install jeremylongshore/claude-code-plugins-nixtla/optimizing-deep-learning-models
skilz install jeremylongshore/claude-code-plugins-nixtla/optimizing-deep-learning-models --agent opencode
skilz install jeremylongshore/claude-code-plugins-nixtla/optimizing-deep-learning-models --agent codex
skilz install jeremylongshore/claude-code-plugins-nixtla/optimizing-deep-learning-models --agent gemini

First time? Install Skilz: pip install skilz

Works with 14 AI coding assistants

Cursor, Aider, Copilot, Windsurf, Qwen, Kimi, and more...

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Download Agent Skill ZIP

Extract and copy to ~/.claude/skills/ then restart Claude Desktop

1. Clone the repository:
git clone https://github.com/jeremylongshore/claude-code-plugins-nixtla
2. Copy the agent skill directory:
cp -r claude-code-plugins-nixtla/archive/backups-20251108/skill-structure-cleanup-20251108-073936/plugins/ai-ml/deep-learning-optimizer/skills/deep-learning-optimizer ~/.claude/skills/

Need detailed installation help? Check our platform-specific guides:

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Agentic Skill Details

Type
Technical
Meta-Domain
data ai
Primary Domain
machine learning
Market Score
17.1

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