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Course Outline
Introduction
- Why Neural Machine Translation?
- Borrowing from image recognition techniques
Overview of the Torch and Caffe2 projects
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
Requirements
- Some programming experience is helpful
- Basic understanding of neural networks
- Experience using the command line
Audience
- Localization specialists with a technical background
- Global content managers
- Localization engineers
- Software developers in charge of implementing global content solutions
7 Hours