Dive into the world where innovation meets efficiency with "Machine Learning with C++: Unleashing Algorithmic Power," a comprehensive guide designed to bridge the gap between the theoretical underpinnings of machine learning and the practical applications of C++ programming. This book is your passport to mastering the art of implementing sophisticated machine learning algorithms in the high-performance environment of C++.
Embark on a journey through the intricacies of machine learning, from foundational concepts to advanced techniques, all through the lens of C++. With this book, you'll not only grasp the theories that drive machine learning forward but also learn how to apply these concepts in a real-world C++ environment. Whether you're a seasoned developer seeking to expand your skills into the realm of machine learning, or a machine learning enthusiast looking to leverage the speed and efficiency of C++, this book caters to all levels of expertise.
"Machine Learning with C++" meticulously guides you through
Core Principles: Understand the core concepts of machine learning and how they can be mapped efficiently onto C++'s powerful programming paradigm.
Algorithmic Deep Dives: Delve into the implementation of classic machine learning algorithms such as decision trees, neural networks, and support vector machines, all optimized for C++.
Performance Optimization: Learn how to exploit C++'s features to optimize your machine learning applications for speed and performance.
Advanced Techniques: Explore advanced topics such as deep learning and reinforcement learning, and understand how C++ can be used to tackle these complex areas.
With "Machine Learning with C++," you'll discover the synergy between machine learning's analytical power and C++'s performance capabilities. This book not only demystifies the process of implementing machine learning algorithms but also provides a solid foundation, encouraging you to push the boundaries of what's possible.
Whether you aim
Author(s): Johann Strauss, Hayden Van Der Post
Publisher: Reactive Publishing
Year: 2024
Language: English
Pages: 390
Title Page
Contents
Preface
Chapter 1: Understanding Machine Learning Concepts
Chapter 2: Setting Up the C++ Machine Learning Environment
Chapter 3: Data Handling and Preprocessing in Machine Learning with C++
Chapter 4: Deep Learning with C++
Chapter 5: Reinforcement Learning in C++
Step 1: Setting Up the Environment
Step 2: Defining the Agent
Step 3: Learning Process
Step 4: Execution Loop
Chapter 6: Real-world Application Development
Chapter 7: Parallel Computing Basics
Chapter 8: Optimizing Machine Learning Models with C++
Chapter 9: Advanced Techniques and Tools
Additional Resources
C++ Principles
Machine Learning Algorithms
Support Vector Machines (SVM)
Decision Trees and Random Forests
Deep Learning Neural Networks
