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The NVIDIA Developer Program ensures that you have the best resources to do your life’s work. Join now and receive exclusive access to tools and SDKs, technical training and webinars, admittance to early access programs and developer community forums, and unlimited use of NVIDIA On-Demand. Join to receive a free NVIDIA Deep Learning Institute (DLI) course.


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Choose Your Free NVIDIA Deep Learning Institute (DLI) Course

Join the NVIDIA Developer Program and take one of the complimentary technical self-paced courses below.


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6 hours

Introduction to Transformer-Based Natural Language Processing

Learn how transformers are used as the building blocks of modern large language models (LLMs). Then use these models for various natural language processing (NLP) tasks, including text classification, named-entity recognition (NER), author attribution, and question answering.

3 hours

Prompt Engineering With Llama 2

Interact with and prompt engineer Llama 2 models to analyze documents, generate text, and be an AI assistant.


2 hours

Essentials of USD in Omniverse

Explore what makes USD unique and the fundamentals for describing, composing, simulating, and collaborating within 3D worlds.

4 hours

Introduction to Robotic Simulations in Isaac Sim

Tap into the simulation loop of a 3D engine and initialize experiments with objects, robots, and physics logic. This can be done programmatically using NVIDIA Omniverse™ Kit and Pixar USD commands, but the course will use NVIDIA Isaac Sim™ Core to wrap these low-level operations in an object-oriented fashion.

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 CUDA 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 CUDA C/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

High-Performance Computing With Containers

Learn how to reduce complexity and improve portability and efficiency of your code by using a containerized environment for high-performance computing (HPC) application development.

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.

8 hours

Fundamentals of Accelerated Computing With OpenACC

Find out how to write and configure code parallelization with OpenACC, optimize memory movements between the CPU and GPU accelerator, and apply the techniques to accelerate a CPU-only Laplace heat equation to achieve performance gains.


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.

2 hours

RAPIDS Accelerator for Apache Spark

Get a walk-through of the RAPIDS Accelerator for Apache Spark, including running SQL queries on CPU and GPU in Spark and diving into the toolset that helps enable success.


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

Modeling Time-Series Data With Recurrent Neural Networks in Keras

Explore how to classify and forecast time-series data using recurrent neural networks (RNNs), such as modeling a patient’s health over time.

4 hours

Deploying a Model for Inference at Production Scale

Learn how to deploy your own machine learning models on a GPU server.

8 hours

Building Real-Time Video AI Applications

Gain the knowledge and skills needed to enable the real-time transformation of raw video data from widely deployed camera sensors into deep learning-based insights.

2 hours

Introduction to Graph Neural Networks

Learn the basic concepts, models, and applications of graph neural networks.

4 hours

Introduction to Physics-Informed Machine Learning With Modulus

Learn the various building blocks of NVIDIA Modulus, which turbocharges use cases by building physics-based deep learning models that are 100,000X faster than traditional methods and offers high-fidelity simulation results.

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.

2 hours

Integrating Sensors With NVIDIA DRIVE

Find out how to integrate automotive sensors into your applications using NVIDIA DRIVE®.

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Build on your existing technical knowledge or learn a new technology by taking advantage of a free self-paced course.


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