Is it suitable for developers new to machine learning?

Absolutely. It simplifies the complex process of model adaptation by providing best-practice implementation patterns and automated documentation.

Which ML frameworks does this skill support?

The Transfer Learning Adapter primarily generates code for industry-standard frameworks including TensorFlow and PyTorch.

Can I use this for both vision and language models?

Yes, the skill is versatile and supports various architectures such as CNNs for computer vision and Transformers for Natural Language Processing.

How does it handle data validation?

The skill generates code that implements automated checks to ensure your specific dataset matches the input requirements and dimensions of the pre-trained model.

Does it provide performance metrics after training?

Yes, it automatically calculates and reports key performance indicators such as accuracy, precision, recall, and F1-score to assess model effectiveness.

Transfer Learning Adapter

Name: Transfer Learning Adapter
Author: jeremylongshore

byjeremylongshore

•

883

Data Science & ML

Automates the fine-tuning and adaptation of pre-trained machine learning models for specific tasks and datasets.

About

The Transfer Learning Adapter skill streamlines the process of repurposing existing machine learning models, such as ResNet or BERT, for new datasets and tasks. It handles the complete lifecycle of model adaptation, from initial requirement analysis and Python code generation using frameworks like PyTorch or TensorFlow, to data validation, training monitoring, and performance reporting. By automating complex tasks like architecture modification and hyperparameter tuning, it enables developers to achieve high-performance results with significantly less time and computational resources than training from scratch.

Key Features

Automated transfer learning code generation for PyTorch and TensorFlow
Integrated artifact saving and documentation generation
Intelligent data preprocessing and validation for new datasets
Automatic model architecture modification for specific task alignment
Built-in performance monitoring with KPI reporting (Accuracy, F1-score)
883 GitHub stars

Use Cases

Fine-tuning a pre-trained computer vision model for custom image classification
Adapting large language models for specialized sentiment analysis or text classification
Optimizing existing neural networks to improve performance on domain-specific datasets

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add jeremylongshore/claude-code-plugins-plus-skills transfer-learning-adapter

For use in Claude.ai and ChatGPT

Download Skill

GitHub