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

An overview of recommender system algorithms

A recommender system's task is to inform a user about an object that could be the most interesting to them at a given time. Most often, such an object is a product or service, but it may be information—for example, in the form of a recommended news article.

Despite the many existing algorithms, we can divide recommender systems into several basic approaches. The most common are as follows:

  • Summary-based: Non-personal models based on the average product rating
  • Content-based: Models based on the intersection of product descriptions and user interests
  • Collaborative filtering: Models based on interests of similar user groups
  • Matrix factorization: Methods based on the preferences matrix decomposition

The basis of any recommender system is the preferences matrix. The preferences matrix has all users of the service laid on one...