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

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...