hamelsmu/code_search
Jupyter notebook tutorial demonstrating how to build semantic code search using deep learning to map natural language queries to code snippets.
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This repository contains a five-part tutorial showing how to create semantic search for code using deep learning. It uses neural networks to learn embeddings that map both code and natural language queries into a shared vector space, enabling semantic matching. The implementation leverages fastai, Keras, TensorFlow, and PyTorch to train models that capture semantic relationships in source code.
Frequently asked
- What is hamelsmu/code_search?
- Jupyter notebook tutorial demonstrating how to build semantic code search using deep learning to map natural language queries to code snippets.
- Is code_search open source?
- Yes — hamelsmu/code_search is open source, released under the MIT license.
- What language is code_search written in?
- hamelsmu/code_search is primarily written in Jupyter Notebook.
- How popular is code_search?
- hamelsmu/code_search has 491 stars on GitHub.
- Where can I find code_search?
- hamelsmu/code_search is on GitHub at https://github.com/hamelsmu/code_search.