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 dimension reduction methods

The main goal of dimension reduction methods is to make the dimension of the transformed representation correspond with the internal dimension of the data. In other words, it should be similar to the minimum number of variables necessary to express all the possible properties of the data. Reducing the dimension helps mitigate the impact of the curse of dimensionality and other undesirable properties that occur in high-dimensional spaces. As a result, reducing dimensionality can effectively solve problems regarding classification, visualization, and compressing high-dimensional data. It makes sense to apply dimensionality reduction only when particular data is redundant; otherwise, we can lose important information. In other words, if we are able to solve the problem using data of smaller dimensions with the same level of efficiency and...