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

Examples of using C++ libraries to create neural networks

Many machine learning libraries have an API for creating and working with neural networks. All the libraries we used in the previous chaptersShogun, Dlib, and Shark-ML—are supported by neural networks. But there are also specialized frameworks for neural networks; for example, one popular one is the PyTorch framework. The difference between a specialized library and the common purpose libraries is that the specialized one supports more configurable options and supports different network types, layers, and loss functions. Also, specialized libraries usually have more modern instruments, and these instruments are introduced to their APIs more quickly.

In this section, we'll create a simple MLP for a regression task with the Shogun, Dlib, and Shark-ML libraries. We'll also use the PyTorch C++ API to...