Book Image

Codeless Deep Learning with KNIME

By : Kathrin Melcher, KNIME AG, Rosaria Silipo
Book Image

Codeless Deep Learning with KNIME

By: Kathrin Melcher, KNIME AG, Rosaria Silipo

Overview of this book

KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network.
Table of Contents (16 chapters)
1
Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
6
Section 2: Deep Learning Networks
12
Section 3: Deployment and Productionizing

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...