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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 discussed how to deploy machine learning models, especially neural networks, to mobile and cloud platforms. We examined that, on these platforms, we usually need a customized build of the machine learning framework that we used in our project. Mobile platforms use different CPUs, and sometimes, they have specialized neural network accelerator devices, so you need to compile your application and machine learning framework in regards to these architectures. The architectures that are used for cloud machines differ from development environments, and you often use them for two different purposes. The first case is to use powerful machine configuration with GPUs to accelerate the machine learning training process, so you need to build your application while taking the use of one or multiple GPUs into account. The other case is using a cloud machine for inference...

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