Book Image

Expert C++

By : Vardan Grigoryan, Shunguang Wu
5 (1)
Book Image

Expert C++

5 (1)
By: Vardan Grigoryan, Shunguang Wu

Overview of this book

C++ has evolved over the years and the latest release – C++20 – is now available. Since C++11, C++ has been constantly enhancing the language feature set. With the new version, you’ll explore an array of features such as concepts, modules, ranges, and coroutines. This book will be your guide to learning the intricacies of the language, techniques, C++ tools, and the new features introduced in C++20, while also helping you apply these when building modern and resilient software. You’ll start by exploring the latest features of C++, and then move on to advanced techniques such as multithreading, concurrency, debugging, monitoring, and high-performance programming. The book will delve into object-oriented programming principles and the C++ Standard Template Library, and even show you how to create custom templates. After this, you’ll learn about different approaches such as test-driven development (TDD), behavior-driven development (BDD), and domain-driven design (DDD), before taking a look at the coding best practices and design patterns essential for building professional-grade applications. Toward the end of the book, you will gain useful insights into the recent C++ advancements in AI and machine learning. By the end of this C++ programming book, you’ll have gained expertise in real-world application development, including the process of designing complex software.
Table of Contents (22 chapters)
1
Section 1: Under the Hood of C++ Programming
7
Section 2: Designing Robust and Efficient Applications
17
Section 3: C++ in the AI World

Chapter 15

  1. ML stands for machine learning and is a field of study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead.
  2. Supervised learning algorithms (also known as training with an instructor) learn from labeled datasets; that is, each record contains additional information describing the data. Unsupervised learning algorithms are even more complex they process the dataset containing a bunch of features and then try to find useful properties of the features.
  3. ML applications include machine translation, natural language processing, computer vision, and email spam detection.
  4. One of the ways is to add a weight for each outcome, if the weight for the subtract operation overweighs others, it will become the dominant operation.
  5. The purpose of neural networks...