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

Optimizing the Autoencoder Strategy

What is the best value to use for threshold ? In the last section, we adopted based on our experience. However, is this the best value for ? Threshold , in this case, is not automatically optimized via the training procedure. It is just a static parameter external to the training algorithm. In KNIME Analytics Platform, it is also possible to optimize static parameters outside of the Learner nodes.

Optimizing Threshold

Threshold is defined on a separate subset of data, called the optimization set. There are two options here:

  • If an optimization set with labeled fraudulent transactions is available, the value of threshold is optimized against any accuracy measure for fraud detection.
  • If no labeled fraudulent transactions are available in the dataset, the value of threshold is defined as a high percentile of the reconstruction errors on the optimization set.

During the data preparation phase, we generated three data subsets...