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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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...