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Codeless Deep Learning with KNIME

You're reading from Codeless Deep Learning with KNIME

Product type Book

Published in Nov 2020

Publisher Packt

ISBN-13 9781800566613

Pages 384 pages

Edition 1st Edition

Languages

Concepts

Authors (3):

Kathrin Melcher

KNIME AG

Rosaria Silipo

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Table of Contents (16) Chapters

Preface

Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension

Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform

Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform

Chapter 3: Getting Started with Neural Networks

Chapter 4: Building and Training a Feedforward Neural Network

Section 2: Deep Learning Networks

Chapter 5: Autoencoder for Fraud Detection

Chapter 6: Recurrent Neural Networks for Demand Prediction

Chapter 7: Implementing NLP Applications

Chapter 8: Neural Machine Translation

Chapter 9: Convolutional Neural Networks for Image Classification

Section 3: Deployment and Productionizing

Chapter 10: Deploying a Deep Learning Network

Chapter 11: Best Practices and Other Deployment Options

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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...

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Authors (3)

Kathrin Melcher

Kathrin Melcher is a data scientist at KNIME. She holds a master's degree in mathematics from the University of Konstanz, Germany. She joined the evangelism team at KNIME in 2017 and has a strong interest in data science and machine learning algorithms. She enjoys teaching and sharing her data science knowledge with the community, for example, in the book From Excel to KNIME, as well as on various blog posts and at training courses, workshops, and conference presentations.

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KNIME AG

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Rosaria Silipo

Rosaria Silipo, Ph.D., now head of data science evangelism at KNIME, has spent 25+ years in applied AI, predictive analytics, and machine learning at Siemens, Viseca, Nuance Communications, and private consulting. Sharing her practical experience in a broad range of industries and deployments, including IoT, customer intelligence, financial services, social media, and cybersecurity, Rosaria has authored 50+ technical publications, including her recent books Guide to Intelligent Data Science (Springer) and Codeless Deep Learning with KNIME (Packt).

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