You're reading from Codeless Deep Learning with KNIME Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

Product type Paperback

Published in Nov 2020

Publisher Packt

ISBN-13 9781800566613

Length 384 pages

Edition 1st Edition

Languages

Python

Tools

Knime

Concepts

Deep Learning

Authors (3):

Kathrin Melcher

KNIME AG

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 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

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Exploring Text Encoding Techniques for Neural Networks

In Chapter 4, Building and Training a Feedforward Neural Network, you learned that feedforward networks – and all other neural networks as well – are trained on numbers and don't understand nominal values. In this chapter, we want to feed words and characters into neural networks. Therefore, we need to introduce some techniques to encode sequences of words or characters – that is, sequences of nominal values – into sequences of numbers or numerical vectors. In addition, in NLP applications with RNNs, it is mandatory that the order of words or characters in the sequence is retained throughout the text encoding procedure.

Let's have a look at some text encoding techniques before we dive into the NLP case studies.

Index Encoding

In Chapter 4, Building and Training a Feedforward Neural Network, you learned about index encoding for nominal values. The idea was to represent each nominal...