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

Deploying the Fraud Detector

At this point, we have an autoencoder network and a rule with acceptable performance for fraud detection. In this section, we will implement the deployment workflow.

The deployment workflow (Figure 5.11), like all deployment workflows, takes in new transaction data, passes it through the autoencoder, calculates the distance, applies the fraud detection rule, and finally, flags the input transaction as fraud or legitimate.

This workflow, named 02_Autoencoder_for_Fraud_Detection_Deployment, is downloadable from the KNIME Hub: https://hub.knime.com/kathrin/spaces/Codeless%20Deep%20Learning%20with%20KNIME/latest/Chapter%205/:

Figure 5.11 – The deployment workflow

Figure 5.11 – The deployment workflow

Let's have a look at the different parts of the workflow in detail.

Reading Network, New Transactions, and Normalization Parameters

In this workflow, first the autoencoder model is read from the previously saved Keras file, using the Keras...