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

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Karl Mueller Feb 15, 2023

While not a huge problem this book really needs the supplied Docker environment for the examples to work properly.I initially tried to set up the environment myself in my base Linux installation and found that some of the tools used in the book are difficult to find, difficult to compile, etc.Previously I knew nothing about Docker, but it wasn't difficult to learn and it is a useful system to know.It does raise the question about how useful some of the tools can be if they can only ever exist properly in the Docker environment provided with the book. Apart from that I found the book very useful for moving my ML knowledge developed in MATLAB, across to C++ which is the main language I use for development.

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Kindle Customer Dec 24, 2020

While Python normally does the job just fine when it comes to handling ML and more general analytics tasks, I have wanted for a long time to work on these kinds of problems using C++. Unfortunately, it has been very difficult to get started because of a severe lack of educational resources out there. Luckily, this book has finally filled that gap for me.What I really like about the book is that the author has put together a series of very complete examples for each method being discussed. Every program reads in an actual csv file with the data (as opposed to using some form of random number generation to create a toy example), puts it into the right format to be used with the given implementation of an ML method and then puts together a data set that one can use as output. As someone who has not had much experience with C++ outside a classroom setting, I found this extremely helpful, and it has made the material immediately applicable to my work in real life.The book covers just the right amount of theory in each chapter as well before diving into the C++ implementation, making the material accessible to developers who are relatively new to data science (which, as I understand, is actually the main target audience).

Robin T. Wernick Feb 08, 2021

Python has hijacked the Machine Learning territory over the last few years since 2014. This leaves the 'C' languages without a comparable foothold in this arena until this book was published. This book covers the gaping void between the 'C' language trained programmers and the Python Machine Language world. It has the same mathematical introduction theory, but counters with a set of code libraries that work with C++.This book will allow the C++ programmer to expand his programming scope without having to rewrite his entire code base in Python and learn a whole new programming language. Not only will it save enormous amounts of time, but it will also provide and give usage detail for a compatible PyTorch Deep Learning library for C++code use. Now the high performance world of GPU programming is available with a tensor interface to C++ programmers.

Matthew Emerick Jun 15, 2020

Disclaimer: The publisher asked me to review this book and gave me a review copy. I promise to be 100% honest in how I feel about this book, both the good and the less so.Personal Background: My first programming language after I started university was C++, followed by C. I'm glad to see that C++ can be used for ML problems, though I do understand that Python can be the easier choice. I try to keep in mind, however, that most if not all Python ML libraries are written in C/C++ to make it run faster.OverviewTo get the most out of this book, I would recommend that you have at least an intermediate competency of C++ and some basic knowledge of machine learning. The former is far more valuable than the later, in this case, as the author assumes that you know C++. There is no hand holding with the code. However, the author does walk you through ML from the basics to a moderate level.What I Like:This book is broken into four overall sections: Overview of Machine Learning, Machine Learning Algorithms, Advanced Examples, and Production and Deployment Challenges. This is an excellent selection of sections that make the overall book better organized. The first section gives a good overview of machine learning (as the title indicates), including a basic understanding of the math involved, data preproccessing, and general rundown of the considerations for choosing which ML technique you should use.The second section gives all the major ML algorithms that a junior ML developer will need. The book focuses on supervised and unsupervised ML, which is most of what you'll see in a business setting. This section finishes with a chapter on Ensemble Learning, where you use multiple ML algorithms to give you better results. The advanced examples mix and match some other algorithms to give you a basic understanding and a starting point for learning more. The final section looks at model deployment and mobile and cloud considerations. If you're new to machine learning and wish to use C++, this is a book book for it. Especially valuable are the Further Reading sections at the end of every chapter.What I Don't Like:When looking at the code, it was very different from the C++ code I learn nearly two decades ago. With the use of C++17, I faced a steep learning curve to use the code examples. While not a concern in and of itself, the first reference to C++17 I could find is on page 41. As someone who knows and enjoys an older version, this made using the code examples more difficult to me. I understand and agree with using a more recent version of the language, but would have appreciated a warning on the back cover or at least in the preface so that I could do some review first. A book recommendation for learning this version of C++ would have be appreciated as well.In the first chapter, the author divides machine learning up into two categories: supervised and unsupervised learning. While technically correct, there is a third category that doesn't fit well into either one: reinforcement learning. I wouldn't expect the author to delve into that niche sub field, it still should have been mentioned.What I Would Like to See:I really enjoyed this book. It has much to offer anyone with C++ experience. It is well organized and has much useful information. I am very happy to have it as part of my library. I think that a book from this author about C++ ML from Scratch would be interesting.Overall, I give this book a 4.9 out of 5. It's an excellent resource.

George Ford Feb 12, 2023

I originally bought this book with the hopes of being able to get a better grasp on machine learning with c++, since the back cover states: "This book makes machine learning with C++ for beginners easy with its example based approach". It starts off reviewing some of the basics of linear algebra... OK. But then in the next chapter, in an attempt to get you familiar with all of the different libraries, you begin loading data using API's without any background to what those API's do and then how you would use that data.The author tries to familiarize you with a bunch of different libraries, without truly ever really describing the details of any of them. The author will write code to accomplish a task with a given library, and then repeat the same with another library. But, in my opinion, this is done without much insight as to why you are doing what you are doing. Just copying code.The background info on neural networks, although helpful, does not really explain fully how they work, outside of providing the differential equations that are implemented.It is a really tough read to go from cover to cover, and I don't feel like you really grasp much, since too much is trying to be explained with a bunch of tools, but no focus on any given tool.I think it would be much better if someone were to focus on one or two tools (xtensor, libtorch, dlib, etc) and approach the subject in that manner. This way, you are familiarizing yourself with the subject as well as the library you are using

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