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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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Toc

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

Summary

In this chapter, we considered how to load data from CSV, JSON, and HDF5 formats. We saw how to convert the loaded data in the objects suitable to use in different machine learning frameworks. We used the libraries' APIs to convert raw C++ arrays into matrices and higher-level datasets' objects for machine learning algorithms. We looked at how to load and process images with the OpenCV and Dlib libraries. We became familiar with the data normalization process, which is very important for many machine learning algorithms. Also, we saw which normalization techniques are available in machine learning libraries, and we implemented some normalization approaches with linear algebra functions from the Eigen library.

In the following chapter, we will see how to measure a model's performance on different types of data. We will look at special techniques that help...

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