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.