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Codeless Deep Learning with KNIME

Codeless Deep Learning with KNIME

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

Codeless Deep Learning with KNIME

4.5 (10)
By: Kathrin 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

The Demand Prediction Problem

Let's continue then by exploring a demand prediction problem and how it can be treated as a time series analysis problem.

Demand prediction is a task related to the need to make estimates about the future. We all agree that knowing what lies ahead in the future makes life much easier. This is true for life events as well as, for example, the prices of washing machines and refrigerators, or demand for electrical energy in an entire city. Knowing how many bottles of olive oil customers will want tomorrow or next week allows for better restocking plans in retail stores. Knowing of a likely increase in the demand for gas or diesel allows a trucking company to better plan its finances. There are countless examples where this kind of knowledge of the future can be of help.

Demand Prediction

Demand prediction, or demand forecasting, is a big branch of data science. Its goal is to make estimations about future demand using historical data and possibly...

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Codeless Deep Learning with KNIME
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