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Hands-On Machine Learning with C++

Hands-On Machine Learning with C++

By : Kirill Kolodiazhnyi
3.8 (6)
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Hands-On Machine Learning with C++

Hands-On Machine Learning with C++

3.8 (6)
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)
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1
Section 1: Overview of Machine Learning
5
Section 2: Machine Learning Algorithms
12
Section 3: Advanced Examples
15
Section 4: Production and Deployment Challenges

An overview of classification methods

The classification task is one of the basic tasks of applied statistics and machine learning, as well as artificial intelligence (AI) as a whole. This is because classification is one of the most understandable and easy-to-interpret data analysis technologies, and classification rules can be formulated in a natural language. In machine learning, a classification task is solved using supervised algorithms because the classes are defined in advance, and the objects in the training set have class labels. Analytical models that solve a classification task are called classifiers.

Classification is the process of moving an object to a predetermined class based on its formalized features. Each object in this problem is usually represented as a vector in N-dimensional space. Each dimension in that space is a description of one of the features of the...

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