Machine Learning with oneAPI 1st Edition

Catch the future of accelerated machine learning with Machine Learning with oneAPI, 1st Edition by Shriram K. Vasudevan, Nitin Vamsi Dantu, Sini Raj Pulari, and T.S. Murugesh. This practical, modern guide bridges machine learning theory and high-performance implementation across CPUs, GPUs, and other accelerators using the oneAPI programming model.

Dive into clear explanations and real-world examples that demystify heterogeneous computing. You’ll find approachable coverage of core ML and deep learning concepts, plus step-by-step techniques to optimize workflows with oneAPI primitives and libraries. Whether you’re a data scientist, software engineer, or student, the book equips you to scale models efficiently on diverse hardware — valuable for teams in India, Europe, North America, and beyond.

Built for practitioners, the narrative emphasizes hands-on problem solving, performance tuning, and portability. Learn how to profile workloads, migrate kernels between devices, and leverage oneAPI’s ecosystem to accelerate inference and training. The authors’ combined expertise delivers a balanced mix of conceptual clarity and actionable advice, making complex topics accessible without sacrificing depth.

Ideal for developers transitioning to heterogeneous architectures or organizations aiming to future-proof ML deployments, this edition is a practical resource for production-grade performance and cross-platform compatibility. Ready to unlock faster, portable machine learning solutions? Add Machine Learning with oneAPI (1st Edition) to your collection and start accelerating intelligent applications across the hardware spectrum. Order your copy today.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.