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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

Encoder-Decoder Architecture

In this section, we will first introduce the general concept of an encoder-decoder architecture. Afterward, we will focus on how the encoder is used in neural machine translation. In the last two subsections, we will concentrate on how the decoder is applied during training and deployment.

One of the possible structures for neural machine translation is the encoder-decoder network. In Chapter 5, Autoencoder for Fraud Detection, we introduced the concept of a neural network consisting of an encoder and a decoder component. Remember, in the case of an autoencoder, the task of the encoder component is to extract a dense representation of the input, while the task of the decoder component is to recreate the input based on the dense representation given by the encoder.

In the case of encoder-decoder networks for neural machine translation, the task of the encoder is to extract the context of the sentence in the source language (the input sentence) into...

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Codeless Deep Learning with KNIME
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