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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

Parameterizing the Workflow

Let's consider a simple workflow: read the Demographics.csv file, filter all data rows with Gender = M or F, and replace M or F with Male or Female, respectively. Once we have decided whether to work on M or F, the workflow becomes quite simple and includes a File Reader, Row Filter, and String Manipulation node with the replace() function:

  1. Let's add one node that allows us to choose whether to work on M or F records: the String Configuration node. This node generates a flow variable. A flow variable is a parameter that travels with the data flow along the workflow branch and it can be used to overwrite settings in other nodes.
  2. As far as we are concerned, for now, two settings are important in the configuration window of this node: the default value and the variable name. Let's use default value M for now, to work with Gender = M records, and let's name the flow variable gender_variable.
  3. Executing the node creates a Flow...
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