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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

Introducing Autoencoders

In previous chapters, we have seen that neural networks are very powerful algorithms. The power of each network lies in its architecture, activation functions, and regularization terms, plus a few other features. Among the varieties of neural architectures, there is a very versatile one, especially useful for three tasks: detecting unknown events, detecting unexpected events, and reducing the dimensionality of the input space. This neural network is the autoencoder.

Architecture of the Autoencoder

The autoencoder (or autoassociator) is a multilayer feedforward neural network, trained to reproduce the input vector onto the output layer. Like many neural networks, it is trained using the gradient descent algorithm, or one of its modern variations, against a loss function, such as the Mean Squared Error (MSE). It can have as many hidden layers as desired. Regularization terms and other general parameters that are useful for avoiding overfitting or for improving...

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