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Codeless Deep Learning with KNIME

You're reading from  Codeless Deep Learning with KNIME

Product type Book
Published in Nov 2020
Publisher Packt
ISBN-13 9781800566613
Pages 384 pages
Edition 1st Edition
Languages
Authors (3):
Kathrin Melcher Kathrin Melcher
Profile icon Kathrin Melcher
KNIME AG KNIME AG
Rosaria Silipo Rosaria Silipo
Profile icon Rosaria Silipo
View More author details

Table of Contents (16) Chapters

Preface 1. Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
2. Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform 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 explored in this chapter by answering the following questions:

  1. Why are LSTM units suitable for time series analysis?

    a). Because they are faster than classic feedforward networks

    b). Because they can remember past input tensors

    c). Because they use gates

    d). Because they have hidden states

  2. What is the data extraction option to use for partitioning in time series analysis?

    a). Draw randomly

    b). Take from top

    c). Stratified Sampling

    d). Linear Sampling

  3. What is a tensor?

    a). A tensor is a two-dimensional vector.

    b). A tensor is a k-dimensional vector.

    c). A tensor is just a number.

    d). A tensor is a sequence of numbers.

  4. What is the difference between in-sample and out-sample testing?

    a). In-sample testing uses the real past values from the test set to make the predictions. Out-sample testing uses past prediction values to make new predictions.

    b). In-sample testing is more realistic than out-sample testing...

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