Treffer: PYTHON CODE GENERATION USING DEEP LEARNING

Title:
PYTHON CODE GENERATION USING DEEP LEARNING
Authors:
Amirov, Amir, Abdygaliyev, Zhandos
Publisher Information:
Nazarbayev University School of Engineerning and Digital Sciences
Publication Year:
2024
Collection:
Nazarbayev University Repository
Subject Terms:
Type of access: Open Access, Code generation, deep learning, transformer, sequence-to-sequence model
Document Type:
other/unknown material
Language:
English
Relation:
Amirov, A. (2024). Python Code Generation Using Deep Learning. Nazarbayev University School of Engineering and Digital Sciences; http://nur.nu.edu.kz/handle/123456789/7865
Availability:
http://nur.nu.edu.kz/handle/123456789/7865
Rights:
Attribution-NonCommercial-ShareAlike 3.0 United States ; http://creativecommons.org/licenses/by-nc-sa/3.0/us/
Accession Number:
edsbas.4DB93682
Database:
BASE

Weitere Informationen

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.