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, you learned about two more options to deploy your trained deep learning networks: web applications and REST services. We finished the chapter – and the book – with some tips and tricks to successfully work with deep learning in KNIME Analytics Platform.

In the first section of this chapter, you learned how to build web applications using KNIME WebPortal of KNIME Server so that end users can execute their workflows and interact with the web pages comfortably from a web browser.

Next, you learned how to build, deploy, and call REST services using KNIME Server to integrate your deep learning networks into the company's IT infrastructure. You learned about the many options to define the input and output data structure of the REST service, how to inspect the REST API using the open source Swagger tool, and how to trigger the execution of a REST service from within KNIME Analytics Platform.

In the last section, we collated some tips and...