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 introduced a new recurrent neural unit: the LSTM unit. We showed how it is built and trained, and how it can be applied to a time series analysis problem, such as demand prediction.

As an example of a demand prediction problem, we tried to predict the average energy consumed by a cluster of users in the next hour, given the energy used in the previous 200 hours. We showed how to test in-sample and out-sample predictions and some numeric measures commonly used to quantify the prediction error. Demand prediction applied to energy consumption is just one of the many demand prediction use cases. The same approach learned here could be applied to predict the number of customers in a restaurant, the number of visitors to a web site, or the amount of a type of food required in a supermarket.

In this chapter, we also introduced a new loop in KNIME Analytics Platform, the recursive loop, and we mentioned a new visualization node, the Line Plot (Plotly) node...