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 Simple Deployment Workflow

So far, in all the case studies we have explored, we have always performed some kind of preprocessing of the input data, such as encoding categorical features, encoding text, or normalizing data, to name just some of the adopted preprocessing steps. During deployment, the new incoming data must be prepared with the exact same preprocessing as the training data in order to be consistent with the task and with the input that the network expects.

In this section, we use the sentiment analysis case study shown in Chapter 7, Implementing NLP Applications, as an example, and we build two deployment workflows for it. The goal of both workflows is to read new movie reviews from a database, predict the sentiment, and write the prediction into the database.

In the first example, the preprocessing steps are implemented manually into the deployment workflow. In the second example, the Integrated Deployment feature is used.

Building a Deployment Workflow...