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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
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KNIME AG
Rosaria Silipo Rosaria Silipo
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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

Questions and Exercises

Check your level of understanding of the concepts presented in this chapter by answering the following questions:

  1. How can you set the loss function to train your neural network?

    a) By using the Keras Loss Function node

    b) By using the Keras Output Layer node

    c) In the configuration window of the Keras Network Learner node

    d) In the configuration window of the Keras Network Executor node

  2. How can you one-hot encode your features?

    a) By using the One Hot Encoding node

    b) By using the One to Many node 

    c) By creating an integer encoding using the Category to Number node and afterward, the Integer to One Hot Encoding node

    d) By creating an integer encoding, transforming it into a collection cell, and selecting the right conversion

  3. How can you define the number of neurons for the input of your network?

    a) By using a Keras Input Layer node.

    b) By using a Keras Dense Layer node without any input network.

    c) The input dimension is set automatically based on...

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