Students: Learn AI and Data Science on GeForce RTX

Self-driving cars. Healthcare. Factory automation. Telecommunications. Advertising and marketing. Financial services. Climate science. One thing these industries all have in common is that they use artificial intelligence. Today, we’re making it easier than ever for STEM students to learn the in-demand AI skills that they will need to be successful in industry.

As AI continues to develop as one of the world’s most important technologies, impacting nearly every part of the global economy, students should pay attention. The demand for graduates with expertise in artificial intelligence is exploding. Indeed has identified AI as one of the most in demand tech skills in 2022, and Forbes has projected that nearly 100 million jobs involving AI will be created in the next four years. As such, colleges and universities are creating new courses and educational programs focusing on these skills.

In order to prepare for careers, students studying popular STEM disciplines like computer science, data science, information sciences, economics, and related fields need access to the same technology that is used by research and industry. NVIDIA’s GPU-accelerated artificial intelligence platform is the de facto standard for AI development, and has been adopted by universities worldwide.

Today, in conjunction with the release of the latest Game Ready Driver, which includes support for CUDA 12,  NVIDIA is announcing support for the RAPIDS suite of software for machine learning, data analytics, and other AI/ML-related techniques, available now on Windows 11 PCs, powered by NVIDIA GeForce RTX GPUs. And, we’ve made available new Windows 11-based training resources including Learning Deep Learning for students and other AI learners. Now students can design, develop, and evaluate models and applications for deep learning, machine learning, data analytics, and graph analytics, accelerated by powerful GPUs, from the comfort of Windows 11. 

GPUs are tremendous parallel processors, which simply mean they can do a lot of mathematical operations at once. The same technology that makes them great for rendering video games also allows them to crank through the calculations that are needed for artificial intelligence. In fact, GPUs from NVIDIA are credited as one of the three key innovations that led to modern AI.

Students who own laptops powered by GeForce RTX GPUs can now run these workloads on their own computers, which can help them finish coursework faster, not have to rely on school-provided computer labs or cloud credits, and be able to use the same trusted technology stack that is used in research and in industry. And of course, RTX GPUs power student hobbies, supercharging the latest games and energizing creative apps.

Learn more about GeForce RTX for STEM students

Today access to NVIDIA’s AI platform gets easier than ever. We’re announcing support for a core software package and a key learning resource. These were previously only available on Linux, but now come to Windows 11 running on Windows Subsystem for Linux (WSL):

• NVIDIA RAPIDS - NVIDIA’s open-source software libraries and APIs for GPU accelerated data science, including data analytics, graph analytics, geospatial analytics, signal analytics and traditional machine learning. RAPIDS is based on the popular PyData stack.
  
• Learning Deep Learning - Published as part of the NVIDIA Deep Learning Institute (DLI), this book contains fundamental training content that students and learners can use to augment or direct their learning of these powerful techniques.

Bringing this technology to Windows 11 significantly lowers the bar for students to take advantage of it, and gives students more options for how and where to study.

Shown: HP OMEN 16 with GeForce RTX 3060 Laptop GPU, one of the top choices for STEM students.

Whether you are a new student curious about artificial intelligence, or already far along in a degree program, here are some great resources for setting up your own PC to learn and develop for AI.

• Setting up - These instructions help you quickly get your RTX-powered Windows 11 desktop or laptop set up to use the AI and data science training resources that follow.
• Learning Deep Learning - Part of the NVIDIA Deep Learning Institute (DLI), Magnus Ekman’s foundational book takes you from basic techniques to applications of deep learning, one of the most exciting techniques in modern AI,. The book is available in hard copy or electronic format.
• Learning RAPIDS - This page provides additional information on using RAPIDS in WSL, and links to additional resources and tools.

Learn more about how GeForce RTX benefits STEM students here. Get access to learning resources on a wide range of  GPU-related topics here.