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

Preparing the Data

In Chapter 3, Getting Started with Neural Networks, we introduced the backpropagation algorithm, which is used by gradient descent algorithms to train a neural network. These algorithms work on numbers and can't handle nominal/categorical input features or class values. Therefore, nominal input features or nominal output values must be encoded into numerical values if we want the network to make use of them. In this section, we will show several numerical encoding techniques and the corresponding nodes in KNIME Analytics Platform to carry them out.

Besides that, we will also go through many other classic data preprocessing steps to feed machine learning algorithms: creating training, validation, and test sets from the original dataset; normalization; and missing value imputation.

Along the way, we will also show you how to import data, how to perform a few additional data operations, and some commonly used tricks within KNIME Analytics Platform. The workflows...