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

Finding the Tone of Your Customers' Voice – Sentiment Analysis

A common use case for NLP is sentiment analysis. Here, the goal is to identify the underlying emotion in some text, whether positive or negative, and all the nuances in between. Sentiment analysis is implemented in many fields, such as to analyze incoming messages, emails, reviews, recorded conversations, and other similar texts.

Generally, sentiment analysis belongs to a bigger group of NLP applications known as text classification. In the case of sentiment analysis, the goal is to predict the sentiment class.

Another common example of text classification is language detection. Here, the goal is to recognize the text language. In both cases, if we use an RNN for the task, we need to adopt a many-to-one architecture. A many-to-one neural architecture accepts a sequence of inputs at different times, , and uses the final state of the output unit to predict the one single class – that is, sentiment...