NVIDIA DLI Teaching Kit Program

The NVIDIA Deep Learning Institute (DLI) Teaching Kit Program provides access to downloadable teaching materials and online courses to help university educators incorporate GPUs into their curriculum. Co-developed with leading university faculty, Teaching Kits provide full curriculum design coupled with ease-of-use. Educators can bridge academic theory with real-world application to empower next-generation innovators with critical computing skill sets. Most Teaching Kits are now also available as ready-made Canvas LMS courses!

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Teaching Kits

The NVIDIA DLI Teaching Kits include downloadable instructional materials and online courses that provide the foundation for understanding and building hands-on expertise in areas like accelerated computing, data science, deep learning, graphics, and robotics. Each Teaching Kit includes some combination of:

  • Lecture slides
  • Lecture videos
  • Hands-on labs/coding projects/solutions
  • Free online DLI courses with certification
  • eBooks
  • Quiz questions/answers

Teaching Kit program members are eligible to receive codes for free access to DLI online, self-paced training for themselves and their students—a value of up to $90 per course, per student. Each course includes a self-paced learning environment with access to a GPU-accelerated workstation in the cloud. Students only need a web browser and internet connection to get started.


Accelerated Computing

Co-developed with Professor Wen-Mei Hwu and his team at University of Illinois (UIUC) and Professor Sunita Chandrasekaran and her team at University of Delaware, the Accelerated Computing Teaching Kit covers introductory and advanced accelerated parallel computing topics, including:

  • Introduction to CUDA C
  • Memory and Data Locality
  • Thread Execution Efficiency
  • Memory Access Performance
  • Parallel Computation Patterns
  • Efficient Host-Device Data Transfer
  • OpenACC, MPI, OpenCL
  • Unified Memory
  • Dynamic Parallelism
  • Multi-GPU Systems
  • CUDA Library Usage

Available in English, Portuguese and Russian.



Data Science

Co-developed with Professor Polo Chau and his team at Georgia Tech and Professor Xishuang Dong and his team at Prairie View A&M University, the Accelerated Data Science Teaching Kit covers fundamental and advanced topics in data collection and preprocessing, the RAPIDS computing framework, distributed computing, machine learning, and graph analytics. This kit includes the following modules:

  • Introduction to Data Science and RAPIDS
  • Data Collection and Preprocessing (ETL)
  • Data Ethics and Bias in Data Sets
  • Data Integration and Analytics
  • Data Visualization
  • Scalable and Distributed Computing
  • Machine Learning
  • Neural Networks
  • Graph Analytics
  • GPU-accelerated Data Science

Available in English and Japanese (translated by Shiga University).



Deep Learning

Co-developed with Professor Yann LeCun and his team at New York University (NYU), the Deep Learning Teaching Kit covers introductory and advanced deep learning topics, including:

  • Introduction to Machine and Deep Learning
  • Applied Image Classification
  • Applied Object Detection
  • Convolutional Neural Networks
  • Applied Image Segmentation
  • Energy-based Learning
  • Unsupervised Learning
  • Generative Adversarial Networks
  • Recurrent Neural Networks
  • Natural Language Processing

Available in English, Japanese and Russian.



Edge AI and Robotics

Co-developed with Ajit Jaokar and his team from the University of Oxford and Patty Delafuente and her team from the University of Maryland Baltimore County, the Edge AI and Robotics Teaching Kit includes lecture slides and hands-on labs centered around edge AI computing, internet of things (IoT), intelligent video analytics, and autonomous robotics. The kit’s focused modules cover:

  • Introduction to Edge AI
  • Vision Deep Neural Networks (DNNs)
  • Diversity, Ethics, and Security
  • Autonomous Robotics
  • Reinforcement Learning
  • Conversational AI

Available in English and Chinese.



Graphics and Omniverse

Created in consultation with top film and animation schools in our Studio Education Partner Program, these Teaching Kits are designed for college and university educators looking to bring graphics and NVIDIA Omniverse™ - an open platform for virtual collaboration and real-time physically accurate simulation - into the classroom.

  • Media and Entertainment
  • Creating Digital Humans
  • Industrial Metaverse
  • Simulations for Architecture, Engineering, Construction and Operations

Available in English.


Science and Engineering

Co-developed with Professor George Karniadakis and his team at Brown University, this Teaching Kit has dedicated modules for physics-informed machine learning (physics-ML) due to its potential to transform simulation workflows across disciplines, including computational fluid dynamics, biomedicine, structural mechanics, and computational chemistry:

  • Primer on Python and Scientific and Deep Learning Libraries
  • Deep Neural Network Architectures, Training and Optimization
  • Physics-informed Neural Networks
  • Neural Operators
  • Data and Uncertainty Quantification
  • HPC and the Modulus Framework

Available in English.

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DLI Online and Instructor-led Courses

The NVIDIA Deep Learning Institute (DLI) offers hands-on training in AI and accelerated computing to solve real-world problems. Participants can earn certifications to prove subject matter competency and support professional career growth.

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Certified Instructor Program

Experienced educators can get certified as DLI Ambassadors and deliver hands-on DLI workshops to university faculty, students, and researchers at no cost.

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Hardware Grant

The NVIDIA Hardware Grant Program promotes advances in artificial intelligence and data science by partnering with academic institutions around the world to enable researchers and educators with industry-leading hardware and software.

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Applied Research Accelerator Program

The NVIDIA Applied Research Accelerator Program supports researchers with technical guidance, hardware, and funding for projects that can make a real-world impact through deployment into GPU-accelerated applications.

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