Focusing on the development of trading systems, this book provides a comprehensive guide to using C++ algorithms and statistical methods in finance and insurance markets. It explores a case study approach, helping readers navigate the transition from initial ideas and experiments to practical implementation. The text offers insights into building, testing, and fine-tuning trading systems, making it a valuable resource for those looking to enhance their understanding and skills in market trading.
The book explores various data-mining algorithms designed for feature selection and extraction, helping to identify key characteristics from large datasets. It emphasizes techniques that streamline data analysis by focusing on essential features, making it a valuable resource for those looking to enhance their data processing capabilities.
Focusing on the evaluation and enhancement of prediction and classification models, this book offers advanced techniques to accurately assess real-world performance. It covers state-of-the-art algorithms, including committee-based decision making, dataset resampling, and boosting, to improve model robustness. Readers will learn essential methods for building effective models and quantifying their expected behavior in practical applications, ensuring reliable outcomes in various scenarios.
Discover the essential building blocks of the most common forms of deep belief networks. At each step this book provides intuitive motivation, a summary of the most important equations relevant to the topic, and concludes with highly commented code for threaded computation on modern CPUs as well as massive parallel processing on computers with CUDA-capable video display cards. The first of three in a series on C++ and CUDA C deep learning and belief nets, Deep Belief Nets in C++ and CUDA C: Volume 1 shows you how the structure of these elegant models is much closer to that of human brains than traditional neural networks; they have a thought process that is capable of learning abstract concepts built from simpler primitives. As such, you’ll see that a typical deep belief net can learn to recognize complex patterns by optimizing millions of parameters, yet this model can still be resistant to overfitting. All the routines and algorithms presented in the book are available in the code download, which also contains some libraries of related routines. What You Will Learn Employ deep learning using C++ and CUDA C Work with supervised feedforward networks Implement restricted Boltzmann machines Use generative samplings Discover why these are important Who This Book Is For Those who have at least a basic knowledge of neural networks and some prior programming experience, although some C++ and CUDA C is recommended.
