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 8: Neural Machine Translation

In the previous chapter, Chapter 7, Implementing NLP Applications, we introduced several text encoding techniques and used them in three Natural Language Processing (NLP) applications. One of the applications was for free text generation. The result showed that it is possible for a network to learn the structure of a language, so as to generate text in a certain style.

In this chapter, we will build on top of this case study for free text generation and train a neural network to automatically translate sentences from a source language into a target language. To do that, we will use concepts learned from the free text generation network, as well as from the autoencoder introduced in Chapter 5, Autoencoder for Fraud Detection.

We will start by describing the general concept of machine translation, followed by an introduction to the encoder-decoder neural architectures that will be used for neural machine translation. Next, we will discuss all...