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 neural networks

In this section, we will discuss what artificial neural networks are and their building blocks. We will learn how artificial neurons work and how they relate to their biological analogs. We will also discuss how to train neural networks with the backpropagation method, as well as how to deal with the overfitting problem.

A neural network is a sequence of neurons interconnected by synapses. The structure of the neural network came into the world of programming directly from biology. Thanks to this structure, the computer has the ability to analyze and even remember information. In other words, neural networks are based on the human brain, which contains millions of neurons that transmit information in the form of electrical impulses.

Artificial neural networks are inspired by biology because they are composed of elements with similar functionalities...