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Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME

By : KNIME AG , Melcher, Rosaria Silipo
4.5 (10)
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Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME

4.5 (10)
By: KNIME AG , Melcher, 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)
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1
Section 1: Feedforward Neural Networks and KNIME Deep Learning Extension
6
Section 2: Deep Learning Networks
12
Section 3: Deployment and Productionizing

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.

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Codeless Deep Learning with KNIME
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