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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

Deploying Models on Mobile and Cloud Platforms

In this chapter, we'll discuss deploying machine learning models on mobile devices running on both the Android operating system and the Google Cloud Platform (GCP).

Using C++ on mobile devices allows us to make programs faster and more compact. We can utilize as many computational resources as possible because modern compilers can optimize the program concerning the target CPU architecture. C++ doesn't use an additional garbage collector for memory management, which can have a significant impact on program performance. Program size can be reduced because C++ doesn't use an additional VM and is compiled directly to machine code. These facts make C++ the right choice for mobile devices with a limited amount of resources and can be used to solve heavy computational tasks.

Using C++ to implement machine learning models...

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