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Codeless Deep Learning with KNIME

You're reading from Codeless Deep Learning with KNIME Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

Product type Paperback

Published in Nov 2020

Publisher Packt

ISBN-13 9781800566613

Length 384 pages

Edition 1st Edition

Languages

Python

Tools

Knime

Concepts

Deep Learning

Authors (3):

Kathrin Melcher

KNIME AG

Rosaria Silipo

View More author details

Table of Contents (16) Chapters

Preface

1. Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension

2. Chapter 1: Introduction to Deep Learning with KNIME Analytics Platform FREE CHAPTER

3. Chapter 2: Data Access and Preprocessing with KNIME Analytics Platform

4. Chapter 3: Getting Started with Neural Networks

5. Chapter 4: Building and Training a Feedforward Neural Network

6. Section 2: Deep Learning Networks

7. Chapter 5: Autoencoder for Fraud Detection

8. Chapter 6: Recurrent Neural Networks for Demand Prediction

9. Chapter 7: Implementing NLP Applications

10. Chapter 8: Neural Machine Translation

11. Chapter 9: Convolutional Neural Networks for Image Classification

12. Section 3: Deployment and Productionizing

13. Chapter 10: Deploying a Deep Learning Network

14. Chapter 11: Best Practices and Other Deployment Options

15. Other Books You May Enjoy

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Summary

We do not have space in this book to describe more of the many nodes available in KNIME Analytics Platform. We will leave this exploratory task to you.

KNIME Analytics Platform includes more than 2,000 nodes and covers a large variety of functionalities. However, the factotum nodes that work in most situations are much fewer in number, such as, for example, File Reader, Row Filter, GroupBy, Join, Concatenation, Math Formula, String Manipulation, Rule Engine, and more. We have described most of them in this chapter to give you a solid basis to build more complex workflows for deep learning, which we will do in the next chapter.

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Authors (3)

Melcher

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

KNIME AG

See other products by KNIME AG

Rosaria Silipo

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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