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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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Building a Web Application

In this section, we will show you the few steps needed to build a web application using the KNIME software.

After a short introduction to KNIME WebPortal, we will show how to create composite views, how to include them to create interaction points, and how to structure the application into a sequence of web pages as interaction points, following the Guided Analytics principles.

As an example, we will apply what we have learned to build a web application around the deployment workflow of the case study on cancer cell classification described in Chapter 9, Convolutional Neural Networks for Image Classification.

Introduction to KNIME WebPortal

The first step in building a web application is to design and implement the sequence of web-based interaction points within the workflow. In a case study on the classification of cancer cells, our data scientist could build a deployment workflow with two interaction points: one to allow the end user to upload...

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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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