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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

Parsing data formats to C++ data structures

The most popular format for representing structured data is called CSV. This format is just a text file with a two-dimensional table in it whereby values in a row are separated with commas, and rows are placed on every new line. It looks like this:

1, 2, 3, 4
5, 6, 7, 8
9, 10, 11, 12

The advantages of this file format are that it has a straightforward structure, there are many software tools that can process it, it is human-readable, and it is supported on a variety of computer platforms. Disadvantages are a lack of support of multidimensional data and data with complex structuring, as well as slow parsing speed in comparison with binary formats.

Another widely used format is JSON. Although the format contains JavaScript in its abbreviation, we can use it with almost all programming languages. This is a file format with name-value pairs...

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