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

Python

Concepts

Deep Learning

Authors (3):

Kathrin Melcher

KNIME AG

Rosaria Silipo

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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

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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...