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

Summary

In this chapter, we have covered three different topics. We started with a summary of the many options for reading, converting, and writing neural networks.

We then moved on to the deployment of neural networks, using the sentiment analysis case study from Chapter 7, Implementing NLP Applications, as an example. The goal here was to build a workflow that uses the trained neural network to predict the sentiment of new reviews stored in the database. We have shown that a deployment workflow can be assembled in two ways: manually or automatically with Integrated Deployment.

The last section of the chapter dealt with the scalability of network training and execution. In particular, it showed how to exploit the computational power of GPUs when training a neural network.

In the next and last chapter of this book, we will explore further deployment options and best practices when working with deep learning.