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

We have reached the end of this relatively long chapter. Here, we have described three NLP case studies, each one solved by training an LSTM-based RNN applied to a time series prediction kind of problem.

The first case study analyzed movie review texts to extract the sentiment hidden in it. We dealt there with a simplified problem, considering a binary classification (positive versus negative) rather than considering too many nuances of possible user sentiment.

The second case study was language modeling. Training an RNN on a given text corpus with a given style produced a network capable of generating free text in that given style. Depending on the text corpus on which the network is trained, it can produce fairy tales, Shakespearean dialogue, or even rap songs. We showed an example that generates text in fairy tale style. The same workflows can be easily extended with more success to generate rap songs (R. Silipo, AI generated rap songs, CustomerThink, 2019, https:...