Machine learning is actively used across a wide range of fields — from financial analysis to computer vision and robotics. While most solutions are developed in Python, high-performance systems require C++ for its speed, memory control, and ability to work with low-level computations. The book "Hands-On Machine Learning with C++", 2nd Edition by Kirill Koladiazhnyi, is a practical guide to machine learning in C++, covering key algorithms, libraries, and optimization methods.
Download "Hands-On Machine Learning with C++", 2nd Edition in PDF for free to gain deeper insights into algorithms, optimize computation, and integrate ML solutions into high-performance systems!
Who is this book for?
- C++ developers looking to learn machine learning: If you're experienced in C++ but new to ML, this book is a great starting point with clear explanations of key algorithms and how to implement them.
- ML specialists aiming to optimize code: Python is convenient but not always efficient. This guide helps translate performance-critical sections into C++ for significant speed gains.
- High-performance system developers: Machine learning is used in systems where low latency, high throughput, and hardware acceleration are crucial. This book shows how to integrate ML algorithms in such environments.
- Researchers and students: For those seeking a deeper understanding of how ML algorithms work under the hood, this book provides C++ implementation examples to explore their inner mechanics.
How is "Hands-On Machine Learning with C++" different from other C++ books?
- C++-focused: While most ML books are Python-centric, this one dives deep into the advantages and specifics of C++.
- Practical format: Real code examples you can use in your own projects.
- Library usage: In-depth coverage of MLPack, Dlib, TensorRT, and OpenCV.
- Computation optimization: Discusses speeding up model training with SIMD, multithreading, and GPU acceleration.
- Real data use cases: Includes examples with financial, medical, and image datasets.
More About the Author of the Book
FAQ for "Hands-On Machine Learning with C++"
Which libraries are used for machine learning in the book?
The book explores MLPack, Dlib, and Eigen, along with ways to integrate TensorRT for accelerating neural networks. MLPack handles classic ML algorithms, Dlib excels in image processing, and Eigen aids efficient linear algebra operations.
How does C++ ML performance compare to Python?
C++ implementations are significantly faster, especially when using optimized libraries, multithreading, and hardware acceleration. For example, image convolution in Dlib can be 5–10x faster than equivalent NumPy code.
Is the knowledge in this textbook suitable for production use?
Yes, the book focuses on practical application. It discusses optimal data structures, multithreaded algorithm execution, and handling large datasets — all key for real-world systems.
Does the book include examples of GPU acceleration?
Yes, Kirill Koladiazhnyi covers CUDA and TensorRT, showing how to speed up models using NVIDIA GPUs — crucial for neural network training and computer vision.
Is the guide suitable for ML beginners?
It’s best suited for those who already have C++ experience and some understanding of linear algebra, probability, and data handling. Beginners in ML are advised to start with Python-based materials before diving into C++ implementations.
Can the code in the book be used in commercial projects?
Yes, the code examples are open-source and can be adapted for use in your own projects without restrictions.
Does it cover C++/Python interoperability?
Yes, the author explains how to build C++ modules for Python using Pybind11, allowing you to accelerate critical code segments.
Information
| Author: | Kirill Koladiazhnyi | Language: | English |
| Publisher: | Packt Publishing | ISBN-13: | 978-1789955330 |
| Publication Date: | May 15, 2020 | ISBN-10: | 1789955335 |
| Print Length: | 530 pages | Category: | Machine Learning and Artificial Intelligence Books |
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