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

Summary

In this chapter, we discussed approaches for building a fraud detector for credit card transactions in the desperate case when no, or almost no, examples of the fraud class are available. This solution trains a neural autoencoder to reproduce legitimate transactions from the input onto the output layer. Some postprocessing is necessary to set an alarm for the fraud candidate based on the reconstruction error.

In describing this solution, we have introduced the concept of training and deployment applications, components, optimization loops, and switch blocks.

In the next chapter, we will discuss a special family of neural networks, so-called recurrent neural networks, and how they can be used to train neural networks for sequential data.

Questions and Exercises

Check your level of understanding of the concepts presented in this chapter by answering the following questions:

  1. What is the goal of an autoencoder during training?

    a) To reproduce the input to the...