Master AI & GPU Computing with NVIDIA’s Free Courses
Course Duration 2.5 Hours Course Objectives: · Express how AI is transforming society. · Explain terminology and concepts related to AI. · Describe the emergence of generative AI applications and their content creation capabilities. · Detail the evolution of GPU computing and its contribution to the AI revolution. · Try a GenAI application yourself! What will I learn: We begin with an introduction to AI, covering basic concepts and principles. Next, we explore graphical processing units (GPUs) and their crucial role in transitioning AI from concept to reality. The course concludes with a video demo showcasing generative AI (GenAI) in action. By the end of the course, you'll be able to understand basic AI concepts and explore GenAI capabilities, such as image generation, on your own.
About this Course Generative AI describes technologies that are used to generate new content based on a variety of inputs. In recent time, Generative AI involves the use of neural networks to identify patterns and structures within existing data to generate new content. In this course, you will learn Generative AI concepts, applications, as well as the challenges and opportunities in this exciting field. Learning Objectives Upon completion, you will have a basic understanding of Generative AI and be able to more effectively use the various tools built on this technology.
About this Course The power of AI is now in the hands of makers, self-taught developers, and embedded technology enthusiasts everywhere with the NVIDIA Jetson developer kits. This easy-to-use, powerful computer lets you run multiple neural networks in parallel for applications like image classification, object detection, segmentation, and speech processing. In this course, you'll use Jupyter iPython notebooks on your own Jetson to build a deep learning classification project with computer vision models.
About this Course This notebook explores the biological and psychological inspirations to the world's first neural networks. Learning Objectives The goals of this exercise include: Exploring how neural networks use data to learn. Understanding the math behind a neuron.
About this Course AI-based video understanding can unlock insights, whether it’s recognizing a cat in your backyard or optimizing customers’ shopping experiences. The NVIDIA Jetson Nano Developer Kit is an easy-to-use, powerful computer that lets you run multiple neural networks in parallel. This makes it a great platform for an introduction to intelligent video analytics (IVA) applications using the NVIDIA DeepStream SDK. In this course, you'll use JupyterLab notebooks and Python application samples on your Jetson Nano to build new projects that extract meaningful insights from video streams through deep learning video analytics. The techniques you learn from this course can then be applied to your own projects in the future on the Nano or other Jetson platforms at the Edge. Learning Objectives You'll learn how to: Set up your Jetson Nano Build end-to-end DeepStream pipelines to convert raw video input into insightful annotated video output Build alternate input and output sources into your pipeline Configure multiple video streams simultaneously Configure alternate inference engines such as YOLO Upon completion, you'll be able to build DeepStream applications that annotate video streams from various and multiple sources to identify and classify objects, count objects in a crowded scene, and output the result as a live stream or file.
About this Course This course is free for a limited time. The evolution and adoption of large language models (LLMs) have been nothing short of revolutionary, with retrieval-based systems at the forefront of this technological leap. These models are not just tools for automation; they are partners in enhancing productivity, capable of holding informed conversations by interacting with a vast array of tools and documents. This course is designed for those eager to explore the potential of these systems, focusing on practical deployment and the efficient implementation required to manage the considerable demands of both users and deep learning models. As we delve into the intricacies of LLMs, participants will gain insights into advanced orchestration techniques that include internal reasoning, dialog management, and effective tooling strategies. Learning Objectives The goal of the course is to teach participants how to: Compose an LLM system that can interact predictably with a user by leveraging internal and external reasoning components. Design a dialog management and document reasoning system that maintains state and coerces information into structured formats. Leverage embedding models for efficient similarity queries for content retrieval and dialog guardrailing. Implement, modularize, and evaluate a RAG agent that can answer questions about the research papers in its dataset without any fine-tuning. By the end of this workshop, participants will have a solid understanding of RAG agents and the tools necessary to develop their own LLM applications.
About this Course Across industries, modern data science requires large amounts of data to be processed quickly and efficiently. These workloads need to be accelerated to ensure prompt results and increase overall productivity. NVIDIA RAPIDS offers a seamless experience to enable GPU-acceleration for many existing data science tasks with zero code changes. Learning Objectives In this course, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows. By participating in this workshop, you’ll : Understand the benefits of a unified workflow across CPUs and GPUs for data science tasks. Learn how to GPU-accelerate various data processing and machine learning workflows with zero code changes. Experience the significant reduction in processing time when workflows are GPU-accelerated.
What You'll Learn: Explore diverse applications of AI across various industries. Understand concepts like Machine Learning, Deep Leaning, training and inference. Trace the evolution of AI Technologies. From its inception to the revolutionary advances brought by Generative AI, and the role of GPUs. You will become familiar with deep learning frameworks and AI software stack. Learn about considerations when deploying AI workloads on a data center on prem, in the cloud, on a hybrid model, or on a multi-cloud environment.