Book Image

Hands-On Machine Learning with C++

By : Kirill Kolodiazhnyi
Book Image

Hands-On Machine Learning with C++

By: Kirill Kolodiazhnyi

Overview of this book

C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This book makes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples. This book will get you hands-on with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ book, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems.
Table of Contents (19 chapters)
Section 1: Overview of Machine Learning
Section 2: Machine Learning Algorithms
Section 3: Advanced Examples
Section 4: Production and Deployment Challenges

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...