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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

Product type Paperback

Published in May 2020

Publisher Packt

ISBN-13 9781789955330

Length 530 pages

Edition 1st Edition

Languages

C++

Tools

Caffe

Concepts

Deep Learning

Author (1):

Kirill Kolodiazhnyi

View More author details

Table of Contents (19) Chapters

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

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Image classification on Android mobile

There are two popular approaches regarding how to deploy a machine learning model to a mobile device with Android. We can either use the PyTorch framework, which now incorporates Caffe2, or we can use the TensorFlow Lite framework. We'll use the PyTorch framework in this chapter since we have discussed it in the previous chapters.

The mobile version of the PyTorch framework

There is no available binary distribution of PyTorch for mobile devices, so we need to build it from source code. We can do this in the same way as we compile its regular version but with additional CMake parameters to enable mobile mode. You also have to install the Android Native Development Kit (NDK), which...

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Authors (1)

Kirill Kolodiazhnyi

Kirill Kolodiazhnyi

Kirill Kolodiazhnyi is a seasoned software engineer with expertise in custom software development. He has several years of experience building machine learning models and data products using C++. He holds a bachelor degree in Computer Science from the Kharkiv National University of Radio-Electronics. He currently works in Kharkiv, Ukraine where he lives with his wife and daughter.

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