The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems across autonomous vehicles, digital content creation, healthcare, finance, and more. Designed for developers, data scientists, researchers, and students with a technical background, DLI training can be accessed in an instructor-led workshop or online in a self-paced course, complete with certification of competency. The DLI University Ambassador Program enables qualified educators to teach DLI workshops at university campuses and academic conferences to faculty, students, and researchers at no cost, complementing the traditional theoretical approaches to university education in machine learning, data science, AI, and parallel computing.
Introducing the 3rd Edition of "Programming Massively Parallel Processors – a Hands-on Approach". This new edition is the result of a collaboration between GPU computing experts and covers the CUDA computing platform, parallel patterns, case studies and other programming models. Brand new chapters cover Deep Learning, graph search, sparse matrix computation, histogram and merge sort.
The tightly-coupled GPU Teaching Kit contains everything needed to teach university courses and labs with GPUs.
As performance and functionality requirements of interdisciplinary computing applications rise, industry demand for new graduates familiar with accelerated computing with GPUs grows. This webinar introduces a comprehensive set of academic labs and university teaching material for use in courses leveraging introductory and advanced parallel programming concepts. The teaching materials start with the basics and focus on programming GPUs with CUDA, and go on to advanced topics such as optimization, advanced architectural enhancements, and integration of a variety of programming languages.
NVIDIA's GPU Education Outreach Program enables classroom and lab use of NVIDIA technologies. Learn more about how NVIDIA plans to provide teaching materials, real GPU resources and software development tools for academic teaching faculty and system administrators world-wide. We will cover options available to give students access to GPU computing platforms, as well as how educators can access these systems and content. Additionally, we will discuss upcoming education outreach programs and seek feedback on how NVIDIA can help educators more easily teach massively parallel programming to their students or user base.