One of the biggest and most powerful hardware companies in the world is NVIDIA. The firm offers free classes to assist you learn more about generative AI, GPUs, robots, semiconductors, and other topics in addition to its highly sought-after GPUs.
Most significantly, you can finish all of them in less than a day and they are all free of cost.
Let's examine them now.
1. Streamlining Data Science Processes with No Code Modifications
Software, financial, and retail organizations need to handle and analyze data efficiently. Better business choices are made possible by GPUs, which allow quicker insights than traditional CPU-driven processes, which are often laborious.
Building and implementing end-to-end GPU-accelerated data science processes for quick data exploration and production deployment will be covered in this program. GPU-accelerated machine learning methods, such as XGBoost, cuGraph's single-source shortest route, and cuML's KNN, DBSCAN, and logistic regression, may be used with RAPIDSTM-accelerated libraries.
You can get more information about the course at https://learn.nvidia.com/courses/course-detail?course_id=DLI+T-DS-03+V1; course-v1
2. The Meaning of Generative AI
The principles of generative AI are introduced in this free, self-paced online course. Generative AI is the process of producing new information depending on various inputs. Participants will get an understanding of the ideas, uses, difficulties, and future possibilities of generative AI via this course.
Learning goals include generative AI's definition and operation, a summary of its many applications, and a discussion of the potential and difficulties that come with it. To take part, all you need to know is the fundamentals of deep learning and machine learning.
Visit this link to read more about the course in detail: https://learn.nvidia.com/courses/course-detail?course_id=DLI+S-NP-01+V1; course-v1
3. Using Morpheus for Digital Fingerprinting
In only one hour, participants will learn how to create and implement the NVIDIA digital fingerprinting AI process, which offers total data visibility and drastically cuts down on the amount of time needed to identify threats.
The NVIDIA Morpheus AI Framework, which is intended to speed up GPU-based AI applications for filtering, processing, and categorizing massive amounts of streaming cybersecurity data, will be made practical for participants to use.
They will also be introduced to the NVIDIA Triton Inference Server, an open-source program that enables the uniform deployment and operation of AI models across a range of workloads. Although knowledge of the Linux command line and defensive cybersecurity principles is helpful, there are no prerequisites for this lesson.
Check out the course at https://courses.nvidia.com/courses/course-v1:DLI+T-DS-02+V2/ to understand more about it in detail.
4. Developing a Mind in Ten Minutes
This course explores the fundamentals of neural networks using concepts from psychology and biology. Understanding how neural networks use input for learning and the mathematical concepts that underpin a neuron's operation are its two main goals.
Although anybody may run the given code and watch its activities, it is recommended to have a firm understanding of basic Python 3 programming principles, such as functions, loops, dictionaries, and arrays. It's also advised to be knowledgeable with calculating regression lines.
Visit this link to study the course in more detail: https://courses.nvidia.com/courses/course-v1:DLI+T-FX-01+V1/
5. A Brief Overview of CUDA
The foundations of creating highly parallel CUDA kernels intended for NVIDIA GPU execution are covered in this course.
Gaining expertise in a number of critical areas is possible, including: initiating massively parallel CUDA kernels on NVIDIA GPUs; coordinating parallel thread execution for processing large datasets; efficiently handling memory transfers between the CPU and GPU; and utilizing profiling techniques to analyze and improve CUDA code performance.
To discover more about the course, click this link: https://learn.nvidia.com/courses/course-detail?course_id=DLI+T-AC-01+V1 in course-v1
6. Developing a Mind in Ten Minutes
This course explores the fundamentals of neural networks using concepts from psychology and biology. Understanding how neural networks use input for learning and the mathematical concepts that underpin a neuron's operation are its two main goals.
Although anybody may run the given code and watch its activities, it is recommended to have a firm understanding of basic Python 3 programming principles, such as functions, loops, dictionaries, and arrays. It's also advised to be knowledgeable with calculating regression lines.
Visit this link to study the course in more detail: https://courses.nvidia.com/courses/course-v1:DLI+T-FX-01+V1/
7. Boost your LLM with RAG
Facebook AI Research developed Retrieval Augmented Generation (RAG) in 2020 as a way to improve an LLM output without requiring model retraining by integrating real-time, domain-specific data. RAG creates an end-to-end architecture by integrating a response generator and an information retrieval module.
This introduction attempts to provide a basic knowledge of RAG, including its retrieval process and the key elements inside NVIDIA's AI Foundations framework, by drawing on NVIDIA's internal practices. Once you have a firm grasp on these principles, you may begin researching LLM and RAG applications.
Visit this link to study the course in further detail: https://courses.nvidia.com/courses/course-v1:NVIDIA+S-FX-16+v1/
8. How to Begin Using AI on Jetson Nano
With the help of the NVIDIA Jetson Nano Developer Kit, manufacturers, self-taught developers, and fans for embedded technology all around the globe may benefit from artificial intelligence.
Several neural networks may be run concurrently on this user-friendly, high-performance computer, opening up a world of possibilities for applications like segmentation, object identification, picture classification, and audio processing.
During the course, students will build a deep learning classification project using computer vision models using Jupyter iPython notebooks on Jetson Nano.
By the conclusion of the course, students will be able to use the Jetson Nano's capabilities to create their own deep learning models for regression and classification.
To discover more about the course, click this link: https://learn.nvidia.com/courses/course-detail?course_id=DLI+S-RX-02+V2(course-v1)
9. Developing Video AI Use Cases in the Periphery with Jetson Nano
The goal of this self-paced online course is to provide students the tools they need to comprehend videos using AI utilizing the NVIDIA Jetson Nano Developer Kit. Using the NVIDIA DeepStream SDK, participants will investigate intelligent video analytics (IVA) applications via hands-on exercises and Python application examples in JupyterLab notebooks.
The Jetson Nano setup, building end-to-end DeepStream pipelines for video analysis, including different input and output sources, configuring multiple video streams, and using alternative inference engines such as YOLO are all covered in this course.
Basic knowledge of the Linux command line and comprehension of Python 3 programming fundamentals are prerequisites. The course needs particular hardware, such as the Jetson Nano Developer Kit, and makes use of technologies like TensorRT and DeepStream. Multiple-choice questions are used for assessment, and when finished, a certificate is given out.
You will need a suitable power source, microSD card, USB data cable, USB webcam, as well as an NVIDIA Jetson Nano Developer Kit or the 2GB version for this training session.
Check out the course at https://courses.nvidia.com/courses/course-v1:DLI+S-IV-02+V2/ to understand more about it in detail.
10. Create Personalized 3D Scene Modifier Applications on NVIDIA Omniverse
This course provides useful advice on how to use the flexible Omniverse platform to expand and improve 3D tools. Participants will learn how to construct sophisticated tools for generating physically correct virtual environments from the Omniverse developer ecosystem team.
Learners will dig into Python code to create new scene manipulator tools inside Omniverse via self-paced assignments. Launching Omniverse Code, installing and activating extensions, traversing the USD stage hierarchy, and building scale-controlling widget manipulators are among the main learning goals.
In addition, the training covers creating specialized scale manipulators and repairing malfunctioning manipulators. The Python Extension, Visual Studio Code, and Omniverse Code are necessary tools. The bare minimum of hardware consists of a desktop or laptop computer with an AMD Ryzen or Intel i7 Gen 5 CPU and an NVIDIA RTX Enabled GPU with 16GB RAM.
Check out the course at https://courses.nvidia.com/courses/course-v1:DLI+S-OV-06+V1/ to understand more about it in detail.
11. Using USD to Begin Collaborative 3D Workflows
Participants will learn how to create scenes using human-readable Universal Scene Description ASCII (USDA) files throughout this self-paced course.
The course is broken up into two sections: USD Fundamentals, which introduces OpenUSD without the need for programming, and Advanced USD, which creates USD files using Python.
Learn about OpenUSD scene structures and get practical experience with OpenUSD Composition Arcs, where you can use Sublayers to override asset attributes, References to combine assets, and Variants to create different asset states.
Here is the link to learn more about the course details: https://learn.nvidia.com/courses/course-detail?course_id=DLI+S-FX-02+V1
12. Put Together a Basic Robot in Isaac Sim
This course provides a hands-on lesson for putting together a simple mobile robot with two wheels using the Isaac Sim GPU platform's "Assemble a Simple Robot" guide. Throughout the course of the 30-minute lesson, important topics are covered, including setting up joint drives and characteristics for the robot's mobility, importing a USD dummy robot into the simulation environment, as well as connecting a local streaming client to a The multiverse Isaac Sim server.
Participants will also learn how to give the robot more articulations. Participants will be more comfortable with the Isaac Sim interface and the documentation needed to start their own robot simulation projects by the conclusion of the course.
A Windows or Linux computer with sufficient internet connectivity for client/server streaming and the ability to install Omniverse Launcher and other apps are requirements for this course. This 30-minute, free lesson on Omniverse technology will cover several topics.
Check out the course at https://courses.nvidia.com/courses/course-v1:DLI+T-OV-01+V1/ to understand more about it in detail.
13. How to Create USD Applications That Are Open for Industrial Twins
The fundamentals of the Omniverse development platform are covered in this course. Learn how to begin developing 3D tools and apps that provide the capability required to support industrial use cases and processes for gathering and analyzing massive buildings, including warehouses, factories, and other locations.
Developing an application from a kit template, adjusting the program via settings, making and editing extensions, and adding new features to extensions are all part of the learning goals.
Visit this link to read more about the course in detail: https://learn.nvidia.com/courses/course-detail?course_id=DLI+S-OV-13+V1
14. Monitoring Disaster Risk using Satellite Images
The course, which was developed in partnership with the United Nations Satellite Centre, teaches participants how to develop and use deep learning models for automated flood detection with an emphasis on disaster risk monitoring using satellite data. The acquired competencies are intended to save expenses, boost productivity, and raise the efficacy of disaster relief initiatives.
The course will cover how to implement a machine learning workflow, use hardware-accelerated tools to handle massive amounts of satellite imagery data, and use transfer-learning to create deep learning models at a reasonable cost.
The course also addresses the use of deep learning-based inference for flood event identification and response, as well as the deployment of models for analysis in close to real-time. Proficiency with Python 3, a fundamental comprehension of machine learning and deep learning principles, and an enthusiasm for manipulating satellite photos are prerequisites.
Visit this link to study the course in more detail: https://courses.nvidia.com/courses/course-v1:DLI+S-ES-01+V1/
15. An Overview of AI in Data Centers
This course will cover the architecture and background of GPUs as well as AI application cases, machine learning, and deep learning procedures. The course also addresses deployment issues for AI workloads in data centers, including multi-system clusters and infrastructure design, in an approachable style for beginners.
Professionals in data centers, DevOps, system and network administrators, and IT are the target audience for this course.
Visit this link to discover more about the course and its contents: https://www.coursera.org/learn/introduction-ai-data-center
16. The Essentials of Using Open USD
The fundamental ideas of Universal Scene Description (OpenUSD), an open framework for collaboratively creating detailed 3D environments, will be covered in this course.
Learn how to leverage USD for non-destructive procedures, data separation for optimized 3D workflows across several industries, and effective scene assembly using layers.
Key concepts in Layering and Composition, model hierarchy for effective scene structure, and Scene Graph Instancing for enhanced scene performance and organization will also be covered in this workshop.
Visit this link to discover more about the course: https://learn.nvidia.com/courses/course-detail?course_id=DLI+S-OV-15+V1
17. An Introduction to Modulus-based Machine Learning with Physics Background
Time and cost restrictions prevent high-fidelity simulations from being used iteratively in science and engineering for design and optimization.
In order to address these issues, NVIDIA Modulus, a physics machine learning platform, develops deep learning models that can achieve up to 100,000 times faster simulation results than conventional techniques.
Learn how to leverage Modulus's API for physics-driven and data-driven tasks, ranging from multi-physics simulations to deep learning, and how it connects with the Omniverse Platform.
Visit this link to read more about the course in detail: https://learn.nvidia.com/courses/course-detail?course_id=DLI+S-OV-04+V1
18. Overview of DOCA for DPUs
Fast application development is made possible by the DOCA Software Framework in collaboration with BlueField DPUs, revolutionizing networking, security, and storage performance.
The DOCA foundations for accelerated data center computing on DPUs are covered in this self-paced course. Topics covered include visualizing the framework paradigm, researching BlueField DPU specifications, looking at example applications, and seeing possibilities for DPU-accelerated compute.
A foundational understanding is acquired to begin the creation of applications for improved data center services.
Visit this link to read more about the course in detail: https://learn.nvidia.com/courses/course-detail?course_id=DLI+S-NP-01+V1; course-v1
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