NVIDIA is one of the world’s leading AI and machine learning companies. Many people don’t realize that the tech giant also has several courses. These courses can be easily accessed by students or anyone interested. They explore various topics, from Artificial intelligence to Gen AI to Machine Learning. So, to learn about the latest technology, check out the courses NVIDIA offers below.
NVIDIA Courses
Advanced Image Segmentation Techniques for Computer Vision
This course is designed to immerse you in the practical world of image segmentation, a crucial aspect of computer vision. It covers advanced algorithms and techniques for partitioning images into meaningful segments, which are essential for various applications, such as object detection and scene understanding. You will explore classical and modern approaches, including deep learning methods that significantly improve segmentation accuracy.
Gain Practical Skills in Image Segmentation for Computer Vision
Integrating Sensors With NVIDIA DRIVE
Integrate Sensors with NVIDIA DRIVE: A Crucial Skill for Autonomous Driving Systems
The course also includes practical sessions, during which participants will work with real-world data to develop and test integration solutions. By the end of the course, participants will have a solid understanding of how to leverage the NVIDIA DRIVE platform to build and optimize sensor systems for autonomous driving applications.
Introduction to Graph Neural Networks
This introductory course provides a comprehensive overview of graph neural networks (GNNs), a powerful class of models designed to work with graph-structured data. You will learn the fundamental concepts behind GNNs, including their architecture, critical operations, and various GNN models. The course covers theoretical foundations and practical applications of GNNs in social network analysis and molecular chemistry, giving you a clear understanding of their real-world impact.
Hands-on exercises will be a core component, allowing you to implement GNNs and experiment with real datasets. These practical sessions aim to deepen your understanding of how GNNs can be applied to solve complex relational data problems and enhance your skills in deploying these models effectively. By the end of the course, you will feel confident in your ability to apply GNNs to real-world problems.
Introduction to Physics-Informed Machine Learning With NVIDIA Modulus
This course introduces the concept of physics-informed machine learning, using NVIDIA Modulus to integrate physical laws into machine learning models. Participants will learn how to enhance model accuracy and generalization by incorporating known physical principles into the learning process. The course covers essential techniques and methodologies for combining physical knowledge with data-driven approaches.
Practical applications and hands-on projects are included to help participants understand how to apply these techniques in real-world scenarios. By the end of the course, participants will be equipped to leverage NVIDIA Modulus to develop robust models informed by physical laws, enhancing their capability to tackle complex problems in various scientific and engineering fields.
Augment Your LLM Using Retrieval-Augmented Generation
This course explores techniques for improving large language models (LLMs) through retrieval-augmented generation (RAG). Participants will learn how to enhance LLM performance by integrating retrieval mechanisms, allowing the model to access and utilize external information dynamically. The course covers the principles behind RAG, including its implementation and benefits in enhancing language understanding and task generation.
The course includes practical sessions where participants apply RAG techniques to real-world problems. By working on hands-on projects, participants will gain experience augmenting LLMs with retrieval capabilities, ultimately improving their effectiveness in various natural language processing applications.
Building RAG Agents for LLMs
This course focuses on developing retrieval-augmented generation (RAG) agents using large language models (LLMs). Participants will learn how to build and deploy RAG systems to enhance the performance of LLMs in various applications. The course covers the architecture and implementation of RAG agents, including integrating them with LLMs to leverage external knowledge sources effectively.
Practical exercises will be critical, allowing participants to develop and test their RAG agents. Through these hands-on activities, participants will gain experience in creating functional RAG systems and deploying them in real-world scenarios, enhancing their skills in advanced language model applications.
Deploying RAG Pipelines for Production at Scale
This course provides training on deploying retrieval-augmented generation (RAG) pipelines in production environments. Participants will learn strategies for scaling RAG systems to handle large-scale applications efficiently. The course covers best practices for optimizing performance, managing resources, and ensuring robust deployment of RAG pipelines.
Participants will also engage in practical sessions to apply these deployment strategies in real-world scenarios. By the end of the course, participants will have a comprehensive understanding of how to deploy RAG pipelines effectively, preparing them to manage and optimize RAG systems in production settings.
Generative AI Explained
This introductory course offers a detailed overview of generative AI, focusing on the principles and techniques used to create models that generate new data. Participants will learn about various generative models, such as generative adversarial networks (GANs) and variational autoencoders (VAEs), and their applications across domains, including image synthesis and text generation.
The course includes hands-on projects to illustrate the practical applications of generative AI. By engaging in these exercises, participants will better understand how to implement and utilize generative models to create new, synthetic data and enhance their capabilities in various AI-driven tasks.
Introduction to Transformer-Based Natural Language Processing
This course provides a foundational understanding of transformer models and their application in natural language processing (NLP). Participants will learn about the architecture and fundamental concepts of transformers, including self-attention mechanisms and their role in improving NLP tasks such as translation and text generation.
The course features practical exercises that allow participants to implement transformer models and apply them to various NLP problems. Through these hands-on activities, participants will gain experience using transformers to enhance language processing capabilities and address complex language tasks.
Prompt Engineering With Llama 2
In this course, participants will learn techniques for prompt engineering, specifically for the Llama 2 model. The course covers methods for designing effective prompts that can significantly improve the performance of the Llama 2 language model in various natural language tasks. Key topics include prompt formulation, tuning, and evaluation.
Practical sessions will allow participants to experiment with different prompt designs and assess their impact on model performance. By the end of the course, participants will be skilled in crafting and optimizing prompts to enhance the effectiveness of the Llama 2 model in real-world applications.
Synthetic Tabular Data Generation Using Transformers
This course explores the generation of synthetic tabular data using transformer models. Participants will learn methods for creating realistic tabular datasets that can be used for training and testing machine learning models. The course covers various techniques for generating synthetic data that accurately reflects real-world patterns.
Hands-on projects will enable participants to apply these techniques to generate synthetic tabular data and evaluate its effectiveness. By the end of the course, participants will be proficient in using transformers to produce high-quality synthetic datasets for a range of machine-learning applications.
Techniques for Improving the Effectiveness of RAG Systems
This course focuses on advanced techniques for enhancing the effectiveness of retrieval-augmented generation (RAG) systems. Participants will learn strategies for optimizing RAG performance, including fine-tuning retrieval mechanisms and improving integration with generation models. The course covers best practices for maximizing the impact of RAG systems in various applications.
Practical sessions will allow participants to apply these techniques to real-world scenarios, improving their ability to develop and deploy efficient RAG systems. By the end of the course, participants will be equipped with advanced skills to enhance RAG performance and effectiveness.
Assemble a Simple Robot in NVIDIA Isaac Sim™
This course offers practical training in assembling a basic robot using the NVIDIA Isaac Sim. Participants will learn to configure and simulate robotic systems for various tasks, gaining hands-on experience with the simulation environment. The course covers fundamental concepts in robotics and simulation, including robot design and behavior.
Participants will engage in hands-on projects to assemble and test their robotic systems within the simulation framework. By the end of the course, participants will have developed the skills needed to create and simulate basic robots, preparing them for more advanced robotic projects.
Build Beautiful, Custom UI for 3D Tools on NVIDIA Omniverse.
This course teaches participants how to design and develop customized user interfaces for 3D tools using NVIDIA Omniverse. The course covers UI design principles and techniques specific to 3D applications, including creating visually appealing and functional interfaces that enhance user experience.
Practical exercises will allow participants to apply these design principles to build custom UIs for 3D tools. By the end of the course, participants will have the skills to develop and implement bespoke user interfaces for 3D applications within the Omniverse platform.
Building a 3D Product Configurator With OpenUSD and Omniverse
This course focuses on creating a 3D product configurator using OpenUSD and NVIDIA Omniverse. Participants will learn to develop interactive and customizable 3D product displays, leveraging OpenUSD for scene description and Omniverse for visualization and interaction.
Hands-on projects will provide participants with practical experience building and deploying 3D product configurators. By the end of the course, participants will be proficient in using OpenUSD and Omniverse to create sophisticated product configurators that enhance customer engagement and visualization.
Efficient Workflows for NVIDIA Omniverse Using Python
This course explores efficient workflows for NVIDIA Omniverse using Python programming. Participants will learn to leverage Python to automate tasks, manage data, and streamline processes within Omniverse environments. The course covers Python scripting techniques and best practices for enhancing productivity in 3D projects.
Hands-on exercises will enable participants to apply Python scripting to real-world Omniverse scenarios. By the end of the course, participants will be skilled in using Python to optimize workflows and improve efficiency in their Omniverse projects.
Implement High-Performance AI Workflows With NVIDIA DGX Cloud™
This course focuses on implementing high-performance AI workflows using NVIDIA DGX Cloud. Participants will learn how to deploy and manage AI solutions in a cloud environment, utilizing DGX Cloud’s capabilities to handle demanding AI tasks. The course covers strategies for optimizing performance and resource utilization in the cloud.
Practical sessions will provide participants with experience setting up and managing AI workflows in DGX Cloud. By the end of the course, participants will be proficient in using DGX Cloud for high-performance AI applications and cloud-based solutions.
NVIDIA Networking: Advanced Techniques
This course delves into advanced networking techniques utilizing NVIDIA technologies. Participants will explore strategies for optimizing and managing complex network infrastructures, focusing on high-performance networking solutions. The course covers advanced topics in network design and implementation.
Practical sessions will provide participants with experience applying these advanced networking techniques to real-world scenarios. By the end of the course, participants will be adept at managing and optimizing complex network environments using NVIDIA technologies.
Advanced RDMA Techniques
This course focuses on advanced techniques for Remote Direct Memory Access (RDMA), aimed at optimizing data transfers and performance in high-speed networking environments. Participants will learn about various RDMA protocols and methods for enhancing data transfer efficiency and reducing latency.
Hands-on projects will provide practical experience in implementing and optimizing RDMA techniques. By the end of the course, participants will be skilled in applying advanced RDMA methods to improve performance in high-speed networking applications.
Building and Operating a Data Center With NVIDIA
This course provides training on building and operating data centers using NVIDIA technologies. Participants will learn about the design, implementation, and management of data center infrastructures, focusing on leveraging NVIDIA solutions for optimal performance and efficiency.
Practical sessions will cover various aspects of data center operations, including hardware setup and management. By the end of the course, participants will be proficient in building and operating data centers with NVIDIA technologies, preparing them for effective data center management.
Introduction to NVIDIA® Spectrum X™ Networking Technologies
This course introduces NVIDIA Spectrum X networking technologies, focusing on fundamental concepts and techniques for managing high-performance networks. Participants will learn about Spectrum X’s key features and applications, including its role in optimizing network infrastructure.
Practical sessions will provide participants with hands-on experience working with Spectrum X technologies. By the end of the course, participants will be equipped with the knowledge and skills to implement and manage high-performance networks using Spectrum X.
Introduction to NVIDIA® Spectrum X™ Networking Technologies: Hands-On
This hands-on course offers practical experience with NVIDIA Spectrum X networking technologies. Participants will engage in interactive exercises to configure and manage Spectrum X networks, gaining valuable skills in deploying and optimizing high-performance network solutions.
The course emphasizes practical application, allowing participants to work with real-world scenarios and data. By the end of the course, participants will be proficient in using Spectrum X technologies to manage and enhance network performance.
Introduction to Networking With NVIDIA Spectrum X
This course provides an overview of networking fundamentals with NVIDIA Spectrum X technologies. Participants will learn about the basic concepts and techniques for implementing and managing high-performance networks, focusing on Spectrum X’s capabilities.
Hands-on projects will enable participants to apply these networking concepts in practical scenarios. By the end of the course, participants will have a foundational understanding of networking with Spectrum X and be able to implement essential network solutions.
Networking Basics With NVIDIA Spectrum X
An introductory course covering networking basics with NVIDIA Spectrum X technologies. Participants will learn fundamental networking principles and how to apply them using Spectrum X, including setup and configuration for high-performance network environments.
The course includes practical exercises to reinforce learning and provide hands-on experience. By the end of the course, participants will be capable of implementing essential networking solutions with Spectrum X and managing simple network infrastructures.
NVIDIA Spectrum™ X: Advanced Networking Techniques
This course focuses on advanced networking techniques using NVIDIA Spectrum X technologies. Participants will explore sophisticated methods for optimizing and managing high-speed network infrastructures, including advanced configuration and performance-tuning strategies.
Hands-on sessions will allow participants to practice these advanced techniques in real-world scenarios. By the end of the course, participants will be skilled in applying advanced networking techniques with Spectrum X to enhance network performance and efficiency.
NVIDIA Spectrum™ X: Advanced Networking Techniques (Lab Only)
A lab-focused course that provides practical experience with advanced networking techniques using NVIDIA Spectrum X. Participants will engage in hands-on lab sessions to develop and apply sophisticated networking solutions, focusing on performance optimization and advanced configurations.
The course emphasizes practical application, with participants gaining experience in real-world networking scenarios. By the end of the course, participants will be proficient in using Spectrum X for advanced network management and optimization.
NVIDIA Spectrum™ X Networking Technologies
This course offers a comprehensive overview of NVIDIA Spectrum X networking technologies, including their features and capabilities. Participants will learn about the various components of Spectrum X and how they contribute to high-performance network solutions.
Practical sessions will provide participants with hands-on experience deploying and managing Spectrum X technologies. By the end of the course, participants will have a thorough understanding of Spectrum X and its applications in network optimization.
Networking With NVIDIA Spectrum™ X
This course explores networking strategies using NVIDIA Spectrum X technologies, covering essential techniques for managing high-speed networks. Participants will learn about Spectrum X’s features and benefits and how to implement effective network solutions.
Hands-on projects will allow participants to apply these networking strategies in practical scenarios. By the end of the course, participants will be equipped with the skills needed to manage and optimize networks using Spectrum X technologies.
Networking With NVIDIA Spectrum™ X: Hands-On
This hands-on course offers practical experience with NVIDIA Spectrum X networking technologies. Participants will engage in interactive exercises to configure and manage Spectrum X networks, gaining skills in deploying and optimizing high-performance network solutions.
The course emphasizes practical application, providing participants real-world experience in managing networks. By the end of the course, participants will be adept at using Spectrum X technologies to enhance network performance and management.
Develop, Customize, and Publish in NVIDIA Omniverse With Extensions
This course covers developing, customizing, and publishing extensions within NVIDIA Omniverse. Participants will learn how to create custom extensions to enhance the functionality of Omniverse tools and environments, including best practices for extension development and deployment.
Practical sessions will allow participants to develop and publish their extensions within the Omniverse ecosystem. By the end of the course, they will have a comprehensive understanding of how to customize and extend Omniverse capabilities to meet specific needs and applications.
Managing GPUs With NVIDIA NVMesh™
This course covers techniques for managing GPUs using NVIDIA NVMesh technology. It focuses on optimizing GPU performance and resource allocation across multiple nodes. Participants will learn strategies for enhancing GPU efficiency and managing complex GPU setups.
Hands-on exercises will allow participants to apply these management techniques to real-world scenarios. By the end of the course, they will have the skills needed to effectively manage GPUs with NVMesh technology, improving performance and resource utilization.
Introduction to NVIDIA® Spectrum™ Networking Technologies
This introductory course covers NVIDIA Spectrum networking technologies, providing essential knowledge for implementing and managing high-performance networks. Participants will learn about Spectrum Technologies’ core concepts and features, including their network design and optimization applications.
Hands-on exercises will help participants gain practical experience with Spectrum networking technologies. By the end of the course, participants will have a solid understanding of how to use Spectrum technologies to enhance network performance and management.
Building AI-Based Cybersecurity Pipelines
This course focuses on developing cybersecurity solutions using AI technologies. Participants will learn to build AI-based pipelines to detect and mitigate cybersecurity threats. The course covers techniques for handling large volumes of data, integrating AI tools into cybersecurity workflows, and enhancing threat detection capabilities with machine learning.
Participants will gain hands-on experience creating and deploying cybersecurity solutions through practical exercises and real-world scenarios. By the end of the course, they will be able to implement AI-driven cybersecurity pipelines that improve the efficiency and effectiveness of threat detection and response.
Building Conversational AI Applications V2.0
This advanced course is designed for developers interested in creating conversational AI applications. Participants will explore the latest techniques and tools for building sophisticated AI-driven conversational agents. The course covers natural language understanding, dialog management, and integration of conversational AI into various applications.
Hands-on labs provide practical experience in developing and deploying conversational AI solutions. By the end of the course, participants will have the skills to design, implement, and refine conversational agents that offer enhanced user interactions and support.
Building Deep Learning-Based Anti-Fraud Applications (Chinese only)
This course is tailored for developers and data scientists who want to create anti-fraud applications using deep learning techniques. It covers methods for detecting and preventing fraudulent activities by leveraging advanced deep-learning models. Topics include feature extraction, model training, and application deployment.
Participants will engage in practical exercises to build and test anti-fraud applications. By the end of the course, they will have the expertise to implement deep learning-based solutions that effectively identify and mitigate fraudulent activities.
Building RAG Agents With LLMs
This course delves into building Retrieval-Augmented Generation (RAG) agents using large language models (LLMs). Participants will learn to design and implement RAG systems that combine retrieval and generation techniques to enhance the performance of AI-driven applications. The course includes advanced topics such as LLM composition and system integration.
Hands-on projects will allow participants to create and optimize RAG agents. By the end of the course, they will be proficient in using LLMs to build sophisticated RAG systems for various applications.
Building Transformer-Based Natural Language Processing Application
This course covers applying and fine-tuning transformer-based models for natural language processing (NLP) tasks. Participants will explore techniques for adapting transformer models to specific NLP applications, including text classification, entity recognition, and language generation.
Practical exercises will guide participants through training and deploying transformer-based models. By the end of the course, they will have the skills to develop and optimize NLP applications using cutting-edge transformer technologies.
Efficient Large Language Model Customizations
This course teaches techniques for efficiently customizing large language models (LLMs) for specific use cases. Participants will learn methods for prompt engineering and parameter-efficient fine-tuning without retraining models from scratch. The course focuses on practical approaches to adapting LLMs for organizational needs.
Hands-on labs provide experience customizing LLMs using the NVIDIA NeMo framework and other tools. By the end of the course, participants will be able to tailor LLMs effectively for various applications while optimizing computational resources.
Generative AI With Diffusion Models
In this course, participants will explore generative AI techniques using diffusion models. The course covers the fundamentals of text-to-image generation and the construction of diffusion models for creating high-quality images. Participants will learn about the U-Net architecture, denoising techniques, and user control mechanisms.
Practical sessions will provide hands-on experience in building and refining diffusion models. By the end of the course, participants will be equipped to develop advanced generative AI applications for various creative and practical uses.
Introduction to Base Command Manager
This introductory course covers the basics of NVIDIA Base Command Manager. Participants will learn to use Base Command Manager to manage and scale workloads across distributed computing environments. The course includes fundamental concepts, configuration practices, and performance optimization strategies.
Hands-on labs will offer practical experience using Base Command Manager for workload management. By the end of the course, participants will be able to deploy and manage computing resources effectively using Base Command Manager.
Introduction to NVIDIA DOCA™ for DPUs
This course introduces NVIDIA DOCA™ for Data Processing Units (DPUs). Participants will learn about the DOCA™ framework, architecture, and applications in managing data processing tasks. The course covers the basics of DPUs and how DOCA™ can be used to optimize data processing and network functions.
Practical exercises will help participants gain hands-on experience with DOCA™ technologies. By the end of the course, they will have a foundational understanding of how to use DOCA™ to enhance data processing and network management capabilities.
NVIDIA AI Enterprise Administration: Public Training
This course offers public training on administering NVIDIA AI Enterprise solutions. Participants will learn how to manage and configure NVIDIA AI Enterprise environments, focusing on best practices for deployment, monitoring, and optimization. The course covers various aspects of AI infrastructure management.
Hands-on labs provide practical experience with NVIDIA AI Enterprise tools and features. By the end of the course, participants will be equipped to administer AI Enterprise environments effectively and leverage NVIDIA solutions for AI applications.
NVIDIA Base Command™ Manager
This course focuses on the administration of NVIDIA Base Command™ Manager. Participants will learn about the features and functionalities of Base Command Manager, including workload management, scaling, and performance optimization. The course includes detailed coverage of configuring and using Base Command Manager for efficient resource management.
Practical exercises and case studies will help participants apply their knowledge to real-world scenarios. By the end of the course, they will be proficient in managing and optimizing workloads using Base Command Manager.
Network Administration With the NVIDIA Onyx™ Switch System
This course covers the essentials of network administration using the NVIDIA Onyx™ Switch System. Participants will learn about the features and capabilities of the Onyx™ Switch, including network configuration, management, and performance tuning. The course emphasizes best practices for maintaining high-performance network environments.
Hands-on labs provide practical experience in setting up and managing Onyx™ Switch systems. By the end of the course, participants will have the skills to administer and optimize network operations effectively using Onyx™ Switch technology.
RDMA Over Converged Ethernet (RoCE) From A to Z
This course comprehensively overviews RDMA over Converged Ethernet (RoCE) technology. Participants will learn about the principles and applications of RoCE, including its role in high-performance computing and data center environments. The course covers the setup, configuration, and optimization of RoCE networks.
Practical exercises will offer hands-on experience in deploying and managing RoCE technology. By the end of the course, participants will be equipped to implement and optimize RDMA networks for high-speed data transfer.
The Fundamentals of RDMA Programming
This course introduces participants to the programming fundamentals of RDMA (Remote Direct Memory Access). The course covers the basic concepts and techniques of RDMA programming, including memory management, data transfer, and performance optimization. Participants will learn how to implement RDMA solutions for efficient data communication.
Hands-on labs provide practical experience in developing and debugging RDMA applications. By the end of the course, participants will have a solid understanding of RDMA programming principles and be capable of applying these techniques in real-world scenarios.
Ansible Essentials for Network Engineers
This course covers the essentials of using Ansible for network automation. Participants will learn how to leverage Ansible to automate network configuration, management, and deployment tasks. The course includes an overview of Ansible playbooks, modules, and best practices for network automation.
Practical exercises will help participants apply Ansible to real-world network automation scenarios. By the end of the course, they will be proficient in using Ansible to streamline network operations and improve efficiency.
Introduction to Networking
This course provides a foundational introduction to networking principles and practices. Participants will learn basic networking concepts, including network topologies, protocols, and technologies. The course covers essential topics such as IP addressing, routing, and network troubleshooting.
Hands-on labs offer practical experience in configuring and managing network components. By the end of the course, participants will have a solid understanding of networking fundamentals and be capable of applying these concepts in various networking environments.
Introduction to Robotic Simulations in NVIDIA Isaac Sim
This course introduces participants to robotic simulations using the NVIDIA Isaac Sim. Participants will learn how to create and simulate robotic environments using Isaac Sim, including configuring simulations, developing robot behaviors, and analyzing simulation results. The course emphasizes practical applications and real-world scenarios.
Hands-on labs provide experience with Isaac Sim tools and features. By the end of the course, participants will be proficient in using Isaac Sim for robotic simulation and development.
Synthetic Data Generation for Training Computer Vision Models
This course focuses on generating synthetic data for training computer vision models. Participants will learn techniques for creating high-quality synthetic datasets that can be used to train and evaluate computer vision algorithms. The course covers data generation methods, model training, and evaluation strategies.
Practical exercises offer hands-on experience generating and using synthetic data for computer vision applications. By the end of the course, participants will have the skills to create adequate synthetic datasets and apply them to improve model performance.
Introduction to NVIDIA Modulus
This course introduces NVIDIA Modulus, a framework for physics-informed machine learning. Participants will learn about Modulus’s key features and capabilities, including its role in integrating physical laws into machine learning models. The course covers setup, configuration, and practical applications of Modulus.
Through hands-on labs, participants will gain experience with Modulus tools and techniques. By the end of the course, they will have a foundational understanding of how to use Modulus for advanced machine learning tasks that incorporate physical principles.
Learn more here: NVIDIA Courses Online.
