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

15. Other Books You May Enjoy

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Generating Free Text with RNNs

Now that we have seen how RNNs can be used for text classification, we can move on to the next case study. Here, we want to train an RNN to generate new free text in a certain style, be it Shakespearean English, a rap song, or mimicking a Brothers Grimm fairy tale. We will focus on the last application: training a network to generate free text in the style of Brothers Grimm fairy tales. However, the network and the process can be easily adjusted to produce a new rap song or a text in old Shakespearean English.

So, how can we train an RNN to generate new text?

The Dataset

First of all, you need a text corpus to train the network to generate new text. Any text corpus is good. However, keep in mind that the text you use for training will define the style of the text automatically generated. If you train the network on Shakespearean theater, you will get new text in old Shakespearean English; if you train the network on rap songs, you will get urban...