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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
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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

Installing KNIME Deep Learning – Keras Integration

In this section, you will learn how to install and set up KNIME Deep Learning - Keras Integration in order to train neural networks in KNIME Analytics Platform.

KNIME Analytics Platform consists of a software core and several provided extensions and integrations. Such extensions and integrations are provided by the KNIME community and extend the original software core through a variety of data science functionalities, including advanced algorithms for AI.

The KNIME extension of interest here is called KNIME Deep Learning – Keras Integration. It offers a codeless GUI-based integration of the Keras library, while using TensorFlow as its backend. This means that a number of functions from Keras libraries have been wrapped into KNIME nodes, within KNIME's classic, easy-to-use visual dialog window. Due to this integration, you can read, write, create, train, and execute deep learning networks without writing code...

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Codeless Deep Learning with KNIME
Published in: Nov 2020
Publisher: Packt
ISBN-13: 9781800566613
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