Hands-On Machine Learning with C++

Build, train, and deploy end-to-end machine learning and deep learning pipelines

Kirill Kolodiazhnyi

• Publisher: Packt Publishing
• Year: 2020
• ISBN:
  9781789952476
• Edition: 1
• Topic: Computer Science
• Subtopic: Natural Language Processing
• Index: Computer Science

Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets

Key Features

• Become familiar with data processing, performance measuring, and model selection using various C++ libraries
• Implement practical machine learning and deep learning techniques to build smart models
• Deploy machine learning models to work on mobile and embedded devices

Book Description

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.

What you will learn

• Explore how to load and preprocess various data types to suitable C++ data structures
• Employ key machine learning algorithms with various C++ libraries
• Understand the grid-search approach to find the best parameters for a machine learning model
• Implement an algorithm for filtering anomalies in user data using Gaussian distribution
• Improve collaborative filtering to deal with dynamic user preferences
• Use C++ libraries and APIs to manage model structures and parameters
• Implement a C++ program to solve image classification tasks with LeNet architecture

Who this book is for

You will find this C++ machine learning book useful if you want to get started with machine learning algorithms and techniques using the popular C++ language. As well as being a useful first course in machine learning with C++, this book will also appeal to data analysts, data scientists, and machine learning developers who are looking to implement different machine learning models in production using varied datasets and examples. Working knowledge of the C++ programming language is mandatory to get started with this book.

Section 1: Overview of Machine Learning

In this section, we will delve into the basics of machine learning with the help of examples in C++ and various machine learning frameworks. We'll demonstrate how to load data from various file formats and describe model performance measuring techniques and the best model selection approaches.

This section comprises the following chapters:

• Chapter 1, Introduction to Machine Learning with C++
• Chapter 2, Data Processing
• Chapter 3, Measuring Performance and Selecting Models

Introduction to Machine Learning with C++

There are different approaches to make computers solve tasks. One of them is to define an explicit algorithm, and another one is to use implicit strategies based on mathematical and statistical methods. Machine Learning (ML) is one of the implicit methods that uses mathematical and statistical approaches to solve tasks. It is an actively growing discipline, and a lot of scientists and researchers find it to be one of the best ways to move forward toward systems acting as human-level artificial intelligence (AI).

In general, ML approaches have the idea of searching patterns in a given dataset as their basis. Consider a recommendation system for a news feed, which provides the user with a personalized feed based on their previous activity or preferences. The software gathers information about the type of news article the user reads and calculates some statistics. For example, it could be the frequency of some topics appearing in a set of news articles. Then, it performs some predictive analytics, identifies general patterns, and uses them to populate the user's news feed. Such systems periodically track a user's activity, and update the dataset and calculate new trends for recommendations.

There are many areas where ML has started to play an important role. It is used for solving enterprise business tasks as well as for scientific researches. In customer relationship management (CRM) systems, ML models are used to analyze sales team activity, to help them to process the most important requests first. ML models are used in business intelligence (BI) and analytics to find essential data points. Human resource (HR) departments use ML models to analyze their employees' characteristics in order to identify the most effective ones and use this information when searching applicants for open positions.

A fast-growing direction of research is self-driving cars, and deep learning neural networks are used extensively in this area. They are used in computer vision systems for object identification as well as for navigation and steering systems, which are necessary for car driving.

Another popular use of ML systems is electronic personal assistants, such as Siri from Apple or Alexa from Amazon. Such products also use deep learning models to analyze natural speech or written text to process users' requests and make a natural response in a relevant context. Such requests can activate music players with preferred songs, as well as update a user's personal schedule or book flight tickets.

This chapter describes what ML is and which tasks can be solved with ML, and discusses different approaches used in ML. It aims to show the minimally required math to start implementing ML algorithms. It also covers how to perform basic linear algebra operations in libraries such as Eigen, xtensor, Shark-ML, Shogun, and Dlib, and also explains the linear regression task as an example.

The following topics will be covered in this chapter:

• Understanding the fundamentals of ML
• An overview of linear algebra
• An overview of a linear regression example

Understanding the fundamentals of ML

There are different approaches to create and train ML models. In this section, we show what these approaches are and how they differ. Apart from the approach we use to create a ML model, there are also parameters that manage how this model behaves in the training and evaluation processes. Model parameters can be divided into two distinct groups, which should be configured in different ways. The last crucial part of the ML process is a technique that we use to train a model. Usually, the training technique uses some numerical optimization algorithm that finds the minimal value of a target function. In ML, the target function is usually called a loss function and is used for penalizing the training algorithm when it makes errors. We discuss these concepts more precisely in the following sections.

Venturing into the techniques of ML

We can divide ML approaches into two techniques, as follows:

• Supervised learning is an approach based on the use of labeled data. Labeled data is a set of known data samples with corresponding known target outputs. Such a kind of data is used to build a model that can predict future outputs.
• Unsupervised learning is an approach that does not require labeled data and can search hidden patterns and structures in an arbitrary kind of data.

Let's have a look at each of the techniques in detail.

Supervised learning

Supervised ML algorithms usually take a limited set of labeled data and build models that can make reasonable predictions for new data. We can split supervised learning algorithms into two main parts, classification and regression techniques, described as follows:

• Classification models predict some finite and distinct types of categories—this could be a...