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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

Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform

Before deep-diving into neural networks and deep learning architectures, it might be a good idea to get familiar with KNIME Analytics Platform and its most important functions.

In this chapter, we will cover a few basic operations within KNIME Analytics Platform. Since every project needs data, we will first go through the basics of how to access data: from files or databases. In KNIME Analytics Platform, you can also access data from REST services, cloud repositories, specific industry formats, and more. We will leave the exploration of these other options to you.

Data comes in a number of shapes and types. In the Data Types and Conversions section, we will briefly investigate the tabular nature of the KNIME data representation, the basic types of data in a data table, and how to convert from one type to another.

At this point, after we have imported the data into a KNIME workflow, we will show some basic...

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