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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

Summary

In this chapter, you learned about two more options to deploy your trained deep learning networks: web applications and REST services. We finished the chapter – and the book – with some tips and tricks to successfully work with deep learning in KNIME Analytics Platform.

In the first section of this chapter, you learned how to build web applications using KNIME WebPortal of KNIME Server so that end users can execute their workflows and interact with the web pages comfortably from a web browser.

Next, you learned how to build, deploy, and call REST services using KNIME Server to integrate your deep learning networks into the company's IT infrastructure. You learned about the many options to define the input and output data structure of the REST service, how to inspect the REST API using the open source Swagger tool, and how to trigger the execution of a REST service from within KNIME Analytics Platform.

In the last section, we collated some tips and...

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