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

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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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Transforming Data

We have read the data from files and databases. In this section, we will perform some operations to consolidate, filter, aggregate, and transform them. We will start with consolidation operations.

Joining and Concatenating

The web activity dataset from the old system comes from a CSV file and, after column renaming, consists of two data columns: CustomerKey and First_WebActivity_. First_WebActivity_ ranks how active a customer is on the company's web site: 0 means not active all and 3 means very active.

The web activity dataset from the new web system comes from the SQLite database and consists of three columns: CustomerKey, First_WebActivity_, and Count. Count is just a progressive number associated with the data rows. It is not important for the upcoming analysis. We can decide later whether to remove it or keep it.

It would be nice to have both rankings for the web activity, from the old and the new system, together in one single data table. For...

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

Table of Contents (16) Chapters

Transforming Data

Joining and Concatenating

Authors (3)

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

Table of Contents (16) Chapters

Transforming Data

Joining and Concatenating

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Authors (3)

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