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Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME

By : KNIME AG , Melcher, Rosaria Silipo
4.5 (10)
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Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME

4.5 (10)
By: KNIME AG , Melcher, 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)
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1
Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
6
Section 2: Deep Learning Networks
12
Section 3: Deployment and Productionizing

Chapter 11: Best Practices and Other Deployment Options

In Chapter 10, Deploying a Deep Learning Network, we introduced the concept of deployment and we showed how to build a workflow to apply a network to new data. In this chapter, we will focus on two more deployment options using the KNIME software.

In the first section of this chapter, you will learn how to deploy a deep learning model as a web application so that end users can execute, interact with, and control the application via a web browser. In order to implement a web application, we need to introduce the KNIME WebPortal, a feature of KNIME Server. Components play a central role in the development of web applications since they are used to implement the interaction points according to the Guided Analytics feature of the KNIME software. In this chapter, you will also learn more about components.

Another deployment option to consume a deep learning model is a web service, through a REST interface. Web services have become...

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Codeless Deep Learning with KNIME
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