PYTHON CODE GENERATION USING DEEP LEARNING - Amirov, Amir; Abdygaliyev, Zhandos (Nazarbayev University School of Engineerning and Digital Sciences, 2024)
In this project, it is proposed to develop a sequence-to-sequence model for Python code generation using deep learning. The aim of this project is to investigate the feasibility of using deep learning to generate functional Python code automatically. It discusses the project’s objectives, methodology, initial findings, and ethical considerations. This report references relevant literature. The significance of this project lies in its attempt to offer a model with a specific task and higher efficiency than general-purpose models.
Keywords: Type of access: Open Access; Code generation; deep learning; transformer; sequence-to-sequence model