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Codeless Deep Learning with KNIME

You're reading from   Codeless Deep Learning with KNIME Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

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Product type Paperback
Published in Nov 2020
Publisher Packt
ISBN-13 9781800566613
Length 384 pages
Edition 1st Edition
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Authors (3):
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Kathrin Melcher Kathrin Melcher
Author Profile Icon Kathrin Melcher
Kathrin Melcher
KNIME AG KNIME AG
Author Profile Icon KNIME AG
KNIME AG
Rosaria Silipo Rosaria Silipo
Author Profile Icon Rosaria Silipo
Rosaria Silipo
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Toc

Table of Contents (16) Chapters Close

Preface 1. Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
2. Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform FREE CHAPTER 3. Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform 4. Chapter 3: Getting Started with Neural Networks 5. Chapter 4: Building and Training a Feedforward Neural Network 6. Section 2: Deep Learning Networks
7. Chapter 5: Autoencoder for Fraud Detection 8. Chapter 6: Recurrent Neural Networks for Demand Prediction 9. Chapter 7: Implementing NLP Applications 10. Chapter 8: Neural Machine Translation 11. Chapter 9: Convolutional Neural Networks for Image Classification 12. Section 3: Deployment and Productionizing
13. Chapter 10: Deploying a Deep Learning Network 14. Chapter 11: Best Practices and Other Deployment Options 15. Other Books You May Enjoy

Preparing the Data for the Two Languages

In Chapter 7, Implementing NLP Applications, we talked about the advantages and disadvantages of training neural networks at the character and word levels. As we already have some experience with the character level, we decided to also train this network for automatic translation at the character level.

To train a neural machine translation network, we need a dataset with bilingual sentence pairs for the two languages. Datasets for different language combinations can be downloaded for free at www.manythings.org/anki/. From there, we can download a dataset containing a number of sentences in English and German that are commonly used in everyday life. The dataset consists of two columns only: the original short text in English and the corresponding translation in German.

Figure 8.5 shows you a subset of this dataset to be used as the training set:

Figure 8.5 – Subset of the training set with English and German sentences

Figure 8.5 – Subset of the training set with English and...

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