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

Dimensionality Reduction

In this chapter, we'll go through a number of dimension reduction tasks. We'll look at the conditions in which dimension reduction is required and learn how to use dimension reduction algorithms efficiently in C++ with various libraries. Dimensionality reduction is where you transfer data that has a higher dimension into a new data representation with a lower dimension, all while preserving the most crucial information from the original data. Such a transformation can help us visualize multidimensional space, which can be useful in the data exploration stage or when identifying the most relevant features in dataset samples. Some machine learning (ML) techniques can perform better or faster if our data has a smaller number of features since it can consume fewer computational resources. The main purpose of this kind of transformation is to save...