Optimize deep learning models using Adam, SGD, and learning rate scheduling to improve accuracy and reduce training time. Use when asked to "optimize deep learning model" or "improve model performance". Trigger with phrases like 'optimize', 'performance', or 'speed up'.
5.2
Rating
0
Installs
Machine Learning
Category
The skill provides a reasonable foundation for deep learning optimization with clear use cases and examples. The description adequately covers when to invoke the skill (optimization, performance improvement, speed up). Task knowledge is solid with referenced scripts for model analysis, optimization, validation, and LR scheduling. However, the structure suffers from redundancy (repeated overview sections, multiple README.md files) and generic boilerplate that adds little value. Novelty is moderate - while helpful for organizing optimization workflows, the core techniques (Adam, SGD, LR scheduling) are standard and well-documented in frameworks like PyTorch/TensorFlow, meaning a capable CLI agent could accomplish similar tasks with moderate effort. The skill would benefit from removing duplicate content, providing more specific technical details about optimization strategies, and demonstrating unique value beyond standard ML library capabilities.
jeremylongshore
Skill Author
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