Machine Learning with oneAPI

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

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

Author(s): Shriram K. Vasudevan, Nitin Vamsi Dantu, Sini Raj Pulari, T.S. Murugesh
Publisher: CRC Press
Year: 2023

Language: English
Pages: 210

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Authors
Chapter 1 Intel oneAPI: An Introductory Discussion
Chapter 2 The Intel oneAPI Toolkits: An Exploration
Chapter 3 The Intel DevCloud and Jupyter Notebooks
Chapter 4 What Is Machine Learning?: An Introduction!
Chapter 5 Tools and Pre-requisites
Chapter 6 Supervised Learning
Chapter 7 Support Vector Machines (SVM): An Exploration
Chapter 8 Decision Trees
Chapter 9 Bagging
Chapter 10 Boosting and Stacking
Chapter 11 Clustering Techniques and Principal Component Analysis
Chapter 12 More Intel Tools for Enhanced Development Experience
Index