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

Parsing data formats to C++ data structures

The most popular format for representing structured data is called CSV. This format is just a text file with a two-dimensional table in it whereby values in a row are separated with commas, and rows are placed on every new line. It looks like this:

1, 2, 3, 4
5, 6, 7, 8
9, 10, 11, 12

The advantages of this file format are that it has a straightforward structure, there are many software tools that can process it, it is human-readable, and it is supported on a variety of computer platforms. Disadvantages are a lack of support of multidimensional data and data with complex structuring, as well as slow parsing speed in comparison with binary formats.

Another widely used format is JSON. Although the format contains JavaScript in its abbreviation, we can use it with almost all programming languages. This is a file format with name-value pairs...