Codeless Deep Learning with KNIME: Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

Codeless Deep Learning with KNIME: Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform

English | 2020 | ISBN: 978-1800566613 | 384 Pages | PDF, EPUB, MOBI | 86 MB


Discover how to integrate KNIME Analytics Platform with deep learning libraries to implement artificial intelligence solutions
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