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Join the NVIDIA Developer Program and take one of the complimentary technical self-paced courses below (worth up to $90).
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8 hours
Generative AI with Diffusion Models
Take a deeper dive into denoising diffusion models, which are a popular choice for text-to-image pipelines, with applications in creative content generation, data augmentation, simulation and planning, anomaly detection, drug discovery, personalized recommendations, and more.
3 hours
Synthetic Data Generation for Training Computer Vision Models
Learn how to use synthetic data in cases where data collection is a challenge. NVIDIA Omniverse Replicator streamlines synthetic data generation (SDG) using 3D assets into a single application and offers the ability to modify the appearance and format of the data.
8 hours
Fundamentals of Accelerated Computing With Python
Explore how to use Numba—the just-in-time, type-specializing Python function compiler—to create and launch CUDA® kernels to accelerate Python programs on massively parallel NVIDIA GPUs.
8 hours
Getting Started with Accelerated Computing in Modern CUDA C++
Learn how to accelerate and optimize existing C/C++ CPU-only applications to apply the power of GPUs using the most essential CUDA techniques and the NVIDIA Nsight™ Systems profiler.
2 hours
Optimizing CUDA Machine Learning Codes With Nsight Profiling Tools
Check out NVIDIA Developer Tools, a collection of applications that span desktop and mobile targets and enable developers to build, debug, profile, and develop cutting-edge software using the latest visual computing hardware from NVIDIA. In this course, you’ll learn the effective use of two powerful NVIDIA developer tools: Nsight Systems and Nsight Compute.
2 hours
GPU Acceleration with the C++ Standard Library
Learn to write simple, portable, parallel-first applications using only standard C++ language features that can be compiled without modification to take advantage of NVIDIA GPU-accelerated environments.
6 hours
Accelerating End-to-End Data Science Workflows
Explore how to perform multiple analysis tasks on large datasets using NVIDIA RAPIDS™, a collection of data science libraries that allows end-to-end GPU acceleration for data science workflows.
4 hours
Exploring Adversarial Machine Learning
Learn how to deploy your own machine learning models on a GPU server.
8 hours
Getting Started With Deep Learning
Explore the fundamentals of deep learning by training neural networks and using results to improve performance and capabilities.
2 hours
Introduction to Graph Neural Networks
Learn the basic concepts, models, and applications of graph neural networks.
4 hours
Deploying a Model for Inference at Production Scale
Learn how to deploy your own machine learning models on a GPU server.
2 hours
Get Started With Highly Accurate Custom ASR for Speech AI
Learn to build, train, fine-tune, and deploy a GPU-accelerated automatic speech recognition (ASR) service with NVIDIA® Riva that includes customized features.
Expand Your Knowledge and Sharpen Your Skills
The NVIDIA Developer Program connects you with the best resources to do your life’s work. Join now and receive exclusive access to free tools, extensive learning opportunities, and expert help.
| Developer Resources | Non-Member Access | Developer Program Community Access |
| NVIDIA NIM™ and NVIDIA Omniverse™ for research, development, and testing. | ||
| CUDA Toolkit | ||
| Early Access Programs | ||
| Academic Grant Programs* |
| Deep Learning Institute (DLI) | ||
| On-Demand | Limited Access | |
| Developer Videos and Webinars | Limited Access | |
| GPU Technology Conference (GTC) |
| Developer Newsletter | General Access | Personalized Content |
| Developer Forums | Read-Only | Expert Help, Recognition, and Networking Opportunities |
| Exclusive Developer Events |
| Technical Blogs | Read-Only | |
| Hardware & Product Documentation | ||
| Research Papers | ||
| White Papers & Technical How-Tos | ||
| NVIDIA GPU Cloud [NGC] Catalog |
* Academic grants available for qualified researchers