Pytorch Transformers for Machine Translation

Instructor: Aladdin Persson

Duration: 1.00

This course provides an introduction to using Pytorch Transformers for Machine Translation. It covers data preprocessing, setting up a Transformer network, training the model, fixing errors, and evaluating the model with a BLEU score. It provides a comprehensive overview of the process for those interested in using Pytorch Transformers for Machine Translation. ▼▲

Course Feature Course Overview Course Provider Discussion and Reviews

Go to class

Course Feature

Cost:

Free

Provider:

Youtube

Certificate:

Paid Certification

Language:

English

Start Date:

On-Demand

Course Overview

❗The content presented here is sourced directly from Youtube platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [February 21st, 2023]

This course provides a comprehensive introduction to the Pytorch Transformer library for machine translation. Learners will learn how to use the library to preprocess data, set up a transformer network, and evaluate the model. They will also learn how to fix errors and calculate the BLEU score.
include:
- Applying the Pytorch Transformer library to other natural language processing tasks such as text summarization, question answering, and sentiment analysis.
- Developing more advanced transformer networks for machine translation.
- Exploring other libraries and frameworks for machine translation.
Learning Suggestions for learners include:
- Learning more about natural language processing and machine translation.
- Exploring other libraries and frameworks for machine translation.
- Practicing with different datasets and tasks.
- Developing a deeper understanding of the transformer architecture.
- Learning more about the BLEU score and other evaluation metrics.