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

Data Processing

One of the essential things in machine learning is the data that we use for training. We can gather training data from the processes we work with, or we can take already prepared training data from third-party sources. In any case, we have to store training data in a file format that should satisfy our development requirements. These requirements depend on the task we solve, as well as the data-gathering process. Sometimes, we need to transform data stored in one format to another to satisfy our needs. Examples of such needs are as follows:

  • Increasing human readability to ease communication with engineers
  • The existence of compression possibility to allow data to occupy less space on secondary storage
  • The use of data in the binary form to speed up the parsing process
  • Supporting the complex relations between different parts of data to make precise mirroring of a...