LANGUAGE TRANSLATION USING SEQ2SEQ LEARNING

Project Overview

The main goal of this project is to create and optimize a machine translation model using deep learning techniques. The challenge is to develop a model capable of handling multiple languages, especially Spanish and English, as well as others like Catalan and Finnish, which use the Western alphabet, to produce high-quality translations.

The project focuses on improving model accuracy by experimenting with network architectures, tuning hyperparameters, and reducing overfitting. Additionally, efficient memory management is a priority, addressing memory limitations during training to ensure smooth and uninterrupted progress.

Features

RNN

Seq2seq training

Automatic translation

Datasets

Anki dataset with 139,705 data entries.

Technologies Used

Python

Recurrent Neural Networks

Seq2seq

LSTM

Pytorch

More Information For detailed documentation, refer to the

Project Documentation (PDF)