Book Image

Expert C++ - Second Edition

By : Marcelo Guerra Hahn, Araks Tigranyan, John Asatryan, Vardan Grigoryan, Shunguang Wu
5 (1)
Book Image

Expert C++ - Second Edition

5 (1)
By: Marcelo Guerra Hahn, Araks Tigranyan, John Asatryan, Vardan Grigoryan, Shunguang Wu

Overview of this book

Are you an experienced C++ developer eager to take your skills to the next level? This updated edition of Expert C++ is tailored to propel you toward your goals. This book takes you on a journey of building C++ applications while exploring advanced techniques beyond object-oriented programming. Along the way, you'll get to grips with designing templates, including template metaprogramming, and delve into memory management and smart pointers. Once you have a solid grasp of these foundational concepts, you'll advance to more advanced topics such as data structures with STL containers and explore advanced data structures with C++. Additionally, the book covers essential aspects like functional programming, concurrency, and multithreading, and designing concurrent data structures. It also offers insights into designing world-ready applications, incorporating design patterns, and addressing networking and security concerns. Finally, it adds to your knowledge of debugging and testing and large-scale application design. With Expert C++ as your guide, you'll be empowered to push the boundaries of your C++ expertise and unlock new possibilities in software development.
Table of Contents (24 chapters)
1
Part 1:Under the Hood of C++ Programming
7
Part 2: Designing Robust and Efficient Applications
18
Part 3:C++ in the AI World

Applying machine learning algorithms

Machine learning algorithms are central to data science and artificial intelligence. They use mathematical models and statistical techniques to train computers to learn from data and make predictions or perform informal actions. Machine learning algorithms enable you to extract insights and patterns from large, complex datasets and inform decisions, automatically processing and improving predictive capabilities. Let us examine and discuss some commonly used algorithms.

Machine learning algorithms can be classified into three categories: supervised learning, unsupervised learning, and reinforcement learning.

Supervised learning algorithms learn from labeled training data, where each data point is associated with a corresponding goal or outcome. These algorithms aim to generalize from the training data and make predictions about unseen data. Commonly used supervised learning algorithms include linear regression, decision trees, support vector...