Fairseq: Setting up a CNN-based machine translation system Training Course

Course Code

Fairseq

Duration

7 hours (usually 1 day including breaks)

Requirements

  • Some programming experience is helpful
  • Basic understanding of neural networks
  • Experience using the command line

Overview

Fairseq is an open-source sequence-to-sequence learning toolkit created by Facebok for use in Neural Machine Translation (NMT).

In this training participants will learn how to use Fairseq to carry out translation of sample content.

By the end of this training, participants will have the knowledge and practice needed to implement a live Fairseq based machine translation solution.

Audience

  • Localization specialists with a technical background
  • Global content managers
  • Localization engineers
  • Software developers in charge of implementing global content solutions

Format of the course
    Part lecture, part discussion, heavy hands-on practice

Note

  • If you wish to use specific source and target language content, please contact us to arrange.

Course Outline

Introduction
    Why Neural Machine Translation?

Overview of the Torch project

Overview of a Convolutional Neural Machine Translation model
    Convolutional Sequence to Sequence Learning
    Convolutional Encoder Model for Neural Machine Translation
    Standard LSTM-based model

Overview of training approaches
    About GPUs and CPUs
    Fast beam search generation

Installation and setup

Evaluating pre-trained models

Preprocessing your data

Training the model

Translating

Converting a trained model to use CPU-only operations

Joining to the community

Closing remarks

Testimonials

★★★★★
★★★★★

Bookings, Prices and Enquiries

Guaranteed to run even with a single delegate!

Private Classroom

From £1250

Private Remote

From £1100 (101)

Public Classroom

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