Book Image

Codeless Deep Learning with KNIME

By : Kathrin Melcher, KNIME AG, Rosaria Silipo
Book Image

Codeless Deep Learning with KNIME

By: Kathrin Melcher, KNIME AG, Rosaria Silipo

Overview of this book

KNIME Analytics Platform is an open source software used to create and design data science workflows. This book is a comprehensive guide to the KNIME GUI and KNIME deep learning integration, helping you build neural network models without writing any code. It’ll guide you in building simple and complex neural networks through practical and creative solutions for solving real-world data problems. Starting with an introduction to KNIME Analytics Platform, you’ll get an overview of simple feed-forward networks for solving simple classification problems on relatively small datasets. You’ll then move on to build, train, test, and deploy more complex networks, such as autoencoders, recurrent neural networks (RNNs), long short-term memory (LSTM), and convolutional neural networks (CNNs). In each chapter, depending on the network and use case, you’ll learn how to prepare data, encode incoming data, and apply best practices. By the end of this book, you’ll have learned how to design a variety of different neural architectures and will be able to train, test, and deploy the final network.
Table of Contents (16 chapters)
1
Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
6
Section 2: Deep Learning Networks
12
Section 3: Deployment and Productionizing

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...