NVIDIA, a prominent player in the hardware industry, offers a range of complimentary courses. Alongside their renowned GPUs, they provide resources covering topics such as generative AI, GPU technology, robotics, and chip development.
Of significant note, these courses are accessible at no charge and can be finished within a day. Now, let’s delve into the details.
Delve into the world of generative AI through this self-paced online course, offered free of charge. Participants will gain insights into the fundamentals of generative AI, which revolves around generating new content based on various inputs.
Throughout the course, learners will comprehend the concepts, applications, hurdles, and potential advancements in generative AI. Key learning objectives include defining generative AI and its mechanisms, exploring its diverse applications, and examining the associated challenges and opportunities. No advanced knowledge is required to enroll, only a basic understanding of machine learning and deep learning principles.
Unlocking Digital Fingerprinting with Morpheus
Embark on a one-hour journey into the realm of digital fingerprinting with Morpheus through this comprehensive course. Participants will be introduced to the development and implementation of the NVIDIA digital fingerprinting AI workflow, offering complete data visibility and significantly reducing threat detection time.
Throughout the course, learners will gain practical experience with the NVIDIA Morpheus AI Framework. This framework is meticulously crafted to accelerate GPU-based AI applications, enabling seamless filtering, processing, and classification of large volumes of streaming cybersecurity data.
Moreover, participants will familiarize themselves with the NVIDIA Triton Inference Server, a versatile open-source tool facilitating standardized deployment and execution of AI models across diverse workloads. While no prerequisites are required for this tutorial, a background in defensive cybersecurity concepts and familiarity with the Linux command line are advantageous.
Crafting a Brain in 10 Minutes
Embark on a journey into the core principles of neural networks with this enlightening course. Drawing inspiration from biological and psychological perspectives, participants will explore the foundations of neural networks, gaining insights into how they utilize data for learning purposes.
The course aims to demystify the mathematical principles governing the functionality of neurons, providing participants with a comprehensive understanding of their operations.
While accessible to all, a solid grasp of fundamental Python 3 programming concepts is recommended to fully engage with the provided code and observe its functionalities. Additionally, familiarity with computing regression lines will enhance the learning experience.
Mastering RAG Agents with LLMs
Discover the potential of retrieval-based Large Language Models (LLMs) in this dynamic course, which highlights their capacity to engage in informed conversations, leverage tools, analyze documents, and strategize effectively.
Participants will delve into the effective deployment of agent systems and learn how to scale them to meet the evolving demands of users and customers. Key learning objectives include exploring scalable deployment methods for LLMs and vector databases, understanding the intricacies of microservices, and experimenting with contemporary LangChain paradigms for dialogue management and document retrieval.
Moreover, participants will have the opportunity to gain practical experience with cutting-edge models and acquire insights into productionalization and framework exploration. This course is ideally suited for individuals familiar with LLMs and related composition frameworks like LangChain, possessing intermediate proficiency in Python.
Enhance Your LLM with Retrieval Augmented Generation (RAG)
Discover Retrieval Augmented Generation (RAG), a groundbreaking technique developed by Facebook AI Research in 2020. RAG offers a powerful method to enhance the output of Large Language Models (LLMs) by integrating real-time, domain-specific data without the need for model retraining. By combining an information retrieval module with a response generator, RAG forms an end-to-end architecture that revolutionizes language processing.
This introductory course, inspired by NVIDIA’s internal practices, provides a foundational understanding of RAG, including its retrieval mechanism and essential components within NVIDIA’s AI Foundations framework. By mastering these fundamentals, participants can embark on their journey to explore LLM and RAG applications.
Building Video AI Applications at the Edge with Jetson Nano
Equip yourself with skills in AI-based video understanding using the NVIDIA Jetson Nano Developer Kit through this self-paced online course. Dive into practical exercises and Python application samples in JupyterLab notebooks to explore intelligent video analytics (IVA) applications leveraging the NVIDIA DeepStream SDK.
This course covers setting up the Jetson Nano, constructing end-to-end DeepStream pipelines for video analysis, integrating various input and output sources, configuring multiple video streams, and utilizing alternate inference engines like YOLO. Prerequisites include basic familiarity with the Linux command line and understanding Python 3 programming concepts. The course leverages tools like DeepStream, TensorRT, and requires specific hardware components like the Jetson Nano Developer Kit.
How to Develop Custom 3D Scene Manipulator Tools on NVIDIA Omniverse
Unlock the potential of the adaptable Omniverse platform in extending and enhancing 3D tools through this practical course. Taught by the Omniverse developer ecosystem team, participants will gain skills to develop advanced tools for creating physically accurate virtual worlds.
Through self-paced exercises, learners will delve into Python coding to craft custom scene manipulator tools within Omniverse. Key learning objectives include launching Omniverse Code, installing/enabling extensions, navigating the USD stage hierarchy, and creating widget manipulators for scale control. The course also covers fixing broken manipulators and building specialized scale manipulators. Required tools include Omniverse Code, Visual Studio Code, and the Python Extension. Minimum hardware requirements comprise a desktop or laptop computer equipped with an Intel i7 Gen 5 or AMD Ryzen processor, along with an NVIDIA RTX Enabled GPU with 16GB of memory.
Assemble a Simple Robot in Isaac Sim
Get hands-on experience in assembling a basic two-wheel mobile robot using the ‘Assemble a Simple Robot’ guide within the Isaac Sim GPU platform. This practical tutorial spans approximately 30 minutes and covers key steps such as connecting a local streaming client to an Omniverse Isaac Sim server, loading a USD mock robot into the simulation environment, and configuring joint drives and properties for the robot’s movement. Additionally, participants will learn to add articulations to the robot, gaining familiarity with the Isaac Sim interface and documentation necessary to initiate their own robot simulation projects. Prerequisites include a Windows or Linux computer capable of installing Omniverse Launcher and applications, along with adequate internet bandwidth for client/server streaming. The course is free of charge, with a duration of 30 minutes, focusing on Omniverse technology.
Disaster Risk Monitoring Using Satellite Imagery
In collaboration with the United Nations Satellite Centre, this course focuses on disaster risk monitoring using satellite imagery, teaching participants to create and implement deep learning models for automated flood detection. The skills gained aim to reduce costs, enhance efficiency, and improve the effectiveness of disaster management efforts.
Participants will learn to execute a machine learning workflow, process large satellite imagery data using hardware-accelerated tools, and apply transfer-learning for building cost-effective deep learning models. The course also covers deploying models for near real-time analysis and utilizing deep learning-based inference for flood event detection and response. Prerequisites include proficiency in Python 3, a basic understanding of machine learning and deep learning concepts, and an interest in satellite imagery manipulation.
Introduction to AI in the Data Center
This course provides a comprehensive introduction to AI use cases, machine learning, and deep learning workflows, as well as the architecture and history of GPUs. With a beginner-friendly approach, the course covers deployment considerations for AI workloads in data centers, including infrastructure planning and multi-system clusters. Targeted towards IT professionals, system and network administrators, DevOps, and data center professionals, this course equips learners with essential knowledge to navigate the evolving landscape of AI technology in data centers.
