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 item-based collaborative filtering with C++

Let's look at how we can implement a collaborative filtering recommender system. As a sample dataset for this example, we use the MovieLens dataset provided by GroupLens from the research lab in the Department of Computer Science and Engineering at the University of Minnesota: They provide a full dataset with 20 million movie ratings and a smaller one for education, with 100,000 ratings. We recommend starting with the smaller one because it allows us to see results earlier and detect implementation errors faster.

This dataset consists of several files, but we are only interested in two of them: ratings.csv and movies.csv. The rating file contains lines with the following format: the user ID, the movie ID, the rating, and the timestamp. In this dataset, users made ratings on...