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

Chapter 7: Implementing NLP Applications

In Chapter 6, Recurrent Neural Networks for Demand Prediction, we introduced Recurrent Neural Networks (RNNs) as a family of neural networks that are especially powerful to analyze sequential data. As a case study, we trained a Long Short-Term Memory (LSTM)-based RNN to predict the next value in the time series of consumed electrical energy. However, RNNs are not just suitable for strictly numeric time series, as they have also been applied successfully to other types of time series.

Another field where RNNs are state of the art is Natural Language Processing (NLP). Indeed, RNNs have been applied successfully to text classification, language models, and neural machine translation. In all of these tasks, the time series is a sequence of words or characters, rather than numbers.

In this chapter, we will run a short review of some classic NLP case studies and their RNN-based solutions: a sentiment analysis application, a solution for free...