Название: Machine Learning with oneAPI Автор: Shriram K. Vasudevan, Nitin Vamsi Dantu, Sini Raj Pulari, Издательство: CRC Press Год: 2024 Страниц: 210 Язык: английский Формат: pdf (true) Размер: 26.3 MB
Intel oneAPI is a unified programming model and software development kit (SDK) from Intel that empowers software developers to generate high-performance applications that can run on different devices, comprising CPUs, GPUs, FPGAs, and other accelerators. It lets developers write code once and deploy it on multiple architectures, decreasing the complexity as well as the cost and time of software development. One of the significant strengths of oneAPI is in its capability to support an eclectic range of devices and architectures, including artificial intelligence, high-performance computing, and data analytics. Along with libraries, tools, and compilers, oneAPI makes it cool for developers to create optimized code for an extensive variety of applications, making it an indispensable tool for any developer who wants to create high-performance software and reap the benefit of the latest hardware technologies. The versatility of oneAPI, by means of appropriate theory and practical implementation with the latest tools in machine learning, has been presented in a simple yet effective way in this book that caters to everyone’s needs. Come on, let’s unleash the true power of our code across varied architectures!
Machine Learning (ML) has been growing tremendously in the market and has vast application possibilities and opportunities. Learning ML has become almost inevitable for engineers to achieve the best results and increased productivity. Many tools and software packages are available to make machine learning easier. oneAPI from Intel has been a boon to the market and many applications are being developed with it. This book explores the ML algorithms, concepts and implementation with relevant theoretical explanations and practical implementations with the latest tools which include oneAPI. The content is presented in such a way that it caters to everyone, from novice to expert level.
The installations and practices needed to get used to the Intel DevCloud, Jupyter notebook and then the machine learning workflow, which is one of the most relevant workloads nowadays, are provided. The visualization tools, the classification, regression, bagging and boosting algorithms, along with their relevant implementation codes, offers an enjoyable learning experience while also demonstrating the solid optimized performance of oneAPI. Classification problems are included to help the reader understand the power of optimization, with additional details on Intel tools provided for an enhanced development experience catering to the varying demands of readers.
GitHub links for the codes have been presented for easier accessibility.
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Machine Learning with oneAPI 
