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

Idea of Neural Machine Translation

Automatic translation has been a popular and challenging task for a long time now. The flexibility and ambiguity of the human language make it still one of the most difficult tasks to implement. The same word or phrase can have different meanings depending on the context and, often, there might not be just one correct translation, but many possible ways to translate the same sentence. So, how can a computer learn to translate text from one language into another? Different approaches have been introduced over the years, all with the same goal: to automatically translate sentences or text from a source language into a target language.

The development of automatic translation systems started in the early 1970s with Rule-Based Machine Translation (RBMT). Here, automatic translation was implemented through hand-developed rules and dictionaries by specialized linguists at the lexical, syntactic, and semantic levels of sentences.

In the 1990s, statistical...