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Hands-On Machine Learning with C++

You're reading from   Hands-On Machine Learning with C++ Build, train, and deploy end-to-end machine learning and deep learning pipelines

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Product type Paperback
Published in May 2020
Publisher Packt
ISBN-13 9781789955330
Length 530 pages
Edition 1st Edition
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Author (1):
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Kirill Kolodiazhnyi Kirill Kolodiazhnyi
Author Profile Icon Kirill Kolodiazhnyi
Kirill Kolodiazhnyi
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Table of Contents (19) Chapters Close

Preface 1. Section 1: Overview of Machine Learning
2. Introduction to Machine Learning with C++ FREE CHAPTER 3. Data Processing 4. Measuring Performance and Selecting Models 5. Section 2: Machine Learning Algorithms
6. Clustering 7. Anomaly Detection 8. Dimensionality Reduction 9. Classification 10. Recommender Systems 11. Ensemble Learning 12. Section 3: Advanced Examples
13. Neural Networks for Image Classification 14. Sentiment Analysis with Recurrent Neural Networks 15. Section 4: Production and Deployment Challenges
16. Exporting and Importing Models 17. Deploying Models on Mobile and Cloud Platforms 18. Other Books You May Enjoy

Plotting data with C++

We plot with the plotcpp library, which is a thin wrapper around the gnuplot command-line utility. With this library, we can draw points on a scatter plot or draw lines. The initial step to start plotting with this library is creating an object of the Plot class. Then, we have to specify the output destination of the drawing. We can set the destination with the Plot::SetTerminal() method and this method takes a string with a destination point abbreviation. It can be the qt string value to show the operating system (OS) window with our drawing, or it can be a string with a picture file extension to save a drawing to a file, as in the code sample that follows. Also, we can configure a title of the drawing, the axis labels, and some other parameters with the Plot class methods. However, it does not cover all possible configurations available for gnuplot. In...

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