Название: Practical C++ Machine Learning: Hands-on strategies for developing simple machine learning models using C++ data structures and librariesАвтор: Anais Sutherland
Издательство: GitforGits
Год: November 8, 2024
Страниц: 174
Язык: английский
Формат: pdf, azw3, epub, mobi
Размер: 10.1 MB
This book introduces C++ programmers to the world of Machine Learning. If you know C++ but haven't worked with Machine Learning solutions before, this book is a good place to start learning the basics and experimenting with the language's essential concepts and techniques.
The book starts off by showing you how to set up a development environment and put together some basic neural networks using the Flashlight library. It then covers essential tasks like data preprocessing, model training, and evaluation, with practical examples that show how machine learning works in a C++ context. You will also learn strategies for dealing with common problems like overfitting and performance optimization. The next few chapters get into more complex topics like convolutional neural networks, model deployment, and some key performance tuning techniques. This will help you develop and integrate your own models into applications.
By the end of the book, you will have essential hands-on experience and a better clarity to explore and expand your machine learning knowledge in C++. This book doesn't aim to cover everything, but it does serve as a good starting point for you to confidently dive into the world of machine learning and deep learning.
When it comes to Machine Learning, Python often steals the spotlight due to its simplicity and the vast ecosystem of libraries. However, C++ holds significant potential in the Machine Learning landscape, especially when performance and efficiency are paramount. C++ offers fine-grained control over system resources, allowing developers to optimize algorithms for speed and memory usage. This control is crucial in scenarios where processing large datasets or running complex models requires maximum computational efficiency, such as real-time systems or applications deployed on resource-constrained devices.
One of the positive aspects of using C++ for Machine Learning is its ability to execute code at blazing speeds. C++ compiles down to machine code, which runs directly on the hardware, eliminating the overhead associated with interpreted languages. This makes C++ an excellent choice for performance-critical applications like high-frequency trading systems, where milliseconds can make a significant difference. C++ also supports concurrent and parallel programming paradigms, enabling developers to fully utilize multi-core processors and GPUs for training and running Machine Learning models, thereby reducing execution time significantly.
Key Learnings:
Use Flashlight to set up a C++ environment for machine learning projects.
Implement neural networks from scratch to gain a hands-on understanding.
Preprocess and augment data effectively to improve model performance.
Train and evaluate models using appropriate loss functions and metrics.
Explore overfitting challenges with techniques like regularization and dropout.
Build advanced architectures like ResNet.
Apply transfer learning to leverage pre-trained models.
Deploy models and integrate them into real-world C++ apps.
Implement real-time inference with optimized performance.
Improve performance using GPU acceleration and multi-threading techniques.
Contents:
Скачать Practical C++ Machine Learning: Hands-on strategies for developing simple machine learning models using C++ data structures and libraries