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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

View More author details

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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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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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.

See other products by Kathrin Melcher

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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