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

Chapter 5: Autoencoder for Fraud Detection

At this point in the book, you should already know the basic math and concepts behind neural networks and some deep learning paradigms, as well as the most useful KNIME nodes for data preparation, how to build a neural network, how to train it and test it, and finally, how to evaluate it. We have built together, in Chapter 4, Building and Training a Feedforward Neural Network, two examples of fully connected feedforward neural networks: one to solve a multiclass classification problem on the Iris dataset and one to solve a binary classification problem on the Adult dataset.

Those were two simple examples using quite small datasets, in which all the classes were adequately represented, with just a few hidden layers in the network and a straightforward encoding of the output classes. However, they served their purpose: to teach you how to assemble, train, and apply a neural network in KNIME Analytics Platform.

Now, the time has come to...