Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions
Key Features
- Become well-versed with KNIME Analytics Platform to perform codeless deep learning
- Design and build deep learning workflows quickly and more easily using the KNIME GUI
- Discover different deployment options without using a single line of code with KNIME Analytics Platform
Book Description
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.
What you will learn
- Use various common nodes to transform your data into the right structure suitable for training a neural network
- Understand neural network techniques such as loss functions, backpropagation, and hyperparameters
- Prepare and encode data appropriately to feed it into the network
- Build and train a classic feedforward network
- Develop and optimize an autoencoder network for outlier detection
- Implement deep learning networks such as CNNs, RNNs, and LSTM with the help of practical examples
- Deploy a trained deep learning network on real-world data
Who this book is for
This book is for data analysts, data scientists, and deep learning developers who are not well-versed in Python but want to learn how to use KNIME GUI to build, train, test, and deploy neural networks with different architectures. The practical implementations shown in the book do not require coding or any knowledge of dedicated scripts, so you can easily implement your knowledge into practical applications. No prior experience of using KNIME is required to get started with this book.
Table of Contents
- Introduction to Deep Learning with KNIME Analytics Platform
- Data Access and Preprocessing with KNIME Analytics Platform
- Getting Started with Neural Networks
- Building and Training a Feedforward Neural Network
- Autoencoder for Fraud Detection
- Recurrent Neural Networks for Demand Prediction
- Implementing NLP Applications
- Neural Machine Translation
- Convolutional Neural Networks for Image Classification
- Deploying a Deep Learning Network
- Best Practices and Other Deployment Options
Kathrin Melcher is a data scientist at KNIME. She holds a master's degree in mathematics from the University of Konstanz, Germany. She joined the evangelism team at KNIME in 2017 and has a strong interest in data science and machine learning algorithms. She enjoys teaching and sharing her data science knowledge with the community, for example, in the book From Excel to KNIME, as well as on various blog posts and at training courses, workshops, and conference presentations.
Rosaria Silipo has been working in data analytics since 1992. Currently, she is a principal data scientist at KNIME. In the past, she has held senior positions with Siemens, Viseca AG, and Nuance Communications, and worked as a consultant in a number of data science projects. She holds a Ph.D. in bioengineering from the Politecnico di Milano and a master's degree in electrical engineering from the University of Florence (Italy). She is the author of more than 50 scientific publications, many scientific white papers, and a number of books for data science practitioners.
