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Deep Learning with MXNet Cookbook

You're reading from   Deep Learning with MXNet Cookbook Discover an extensive collection of recipes for creating and implementing AI models on MXNet

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Product type Paperback
Published in Dec 2023
Publisher Packt
ISBN-13 9781800569607
Length 370 pages
Edition 1st Edition
Languages
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Author (1):
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Andrés P. Torres Andrés P. Torres
Author Profile Icon Andrés P. Torres
Andrés P. Torres
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Table of Contents (12) Chapters Close

Preface 1. Chapter 1: Up and Running with MXNet FREE CHAPTER 2. Chapter 2: Working with MXNet and Visualizing Datasets – Gluon and DataLoader 3. Chapter 3: Solving Regression Problems 4. Chapter 4: Solving Classification Problems 5. Chapter 5: Analyzing Images with Computer Vision 6. Chapter 6: Understanding Text with Natural Language Processing 7. Chapter 7: Optimizing Models with Transfer Learning and Fine-Tuning 8. Chapter 8: Improving Training Performance with MXNet 9. Chapter 9: Improving Inference Performance with MXNet 10. Index 11. Other Books You May Enjoy

Improving performance for translating English to German

In the previous recipes, we have seen how we can leverage pre-trained models and new datasets for transfer learning and fine-tuning applied to CV tasks. In this recipe, we will follow a similar approach, but with an NLP task, translating from English to German.

In the fourth recipe, Translating text from Vietnamese to English, in Chapter 6, Understanding Text with Natural Language Processing, we saw how we could use GluonNLP to retrieve pre-trained models and use them directly for a translation task, training them from scratch, effectively only leveraging past knowledge by using the architecture of the pre-trained model.

In this recipe, we will also leverage the weights/parameters of the model, obtained for a task consisting of translating text from English to German using machine translation models. The dataset that we will use for pre-training will be WMT2014 (task source), and we will run several experiments to evaluate...

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