Preface

This book aims to introduce you to the concepts and practices of deep learning networks. A number of case studies based on deep learning solutions are studied. In each case study, a neural architecture is explained and implemented through the codeless KNIME Analytics Platform tool. We start with a brief introduction to the basic concepts of deep learning and the visual programming KNIME Analytics Platform tool. Once the basic concepts are clear, we continue on with case studies on the usage of deep learning architectures to solve specific tasks: a neural autoencoder for fraud detection, recurrent neural networks for demand prediction and natural language processing, an encoder-decoder architecture for neural machine translation, and a convolutional neural network for image classification. The book concludes by describing the deployment options of trained networks and offering a few tips and tricks to train and successfully apply a deep learning network.