NVIDIA has transformed from a company that originally made gaming GPUs to a leader in AI technology. This started when they discovered tensor cores while researching how to improve their graphics cards. These tensor cores are now used to train large language models (LLM).
NVIDIA's technology has revolutionized not only hardware but also software. In particular, CUDA is a software platform for GPU parallel computing, which allows developers to easily utilize GPU performance. CUDA is widely used in various fields such as scientific computing, artificial intelligence, and machine learning, and has become the standard for GPU accelerated computing. Well, it led to a rise in NVIDIA's stock price and a surge in chip demand...
Free NVIDIA lectures released by His Majesty Emperor Jensen
Building on these technical achievements, NVIDIA has started offering several free self-paced courses to educate the general public about AI technologies. These courses are offered through the Deep Learning Institute and include:
Personally, I recommend listening to it in the order below. Personally, it is good enough to understand Jetson Nano, RAPIDS, and CUDA to some extent. It is very satisfying as a sample lecture, but it is a little lacking as a practical lecture. It is good for understanding the terminology and understanding the flow. I think it would be good to try things that can be done as toy projects like Jetson Nano.
NVIDIA also issues paid courses and certifications, so if you're interested, you might want to take a look. As always, the best thing to do is to create something yourself. I'm rooting for you.
NVIDIA Academy Courses Catalog.pdf729KB
Jensen Huang,,, the leather jacket looks even brighter.
This course from Coursera will teach you how to deploy AI workloads in your data center. From speech recognition to improving supply chain management, AI technologies provide the computing power, tools, and algorithms that businesses need to perform their everyday tasks. So how does AI work in your data center? What hardware and software infrastructure is required? This course will cover the concepts and terminology that will help you get started on your journey to AI and GPU computing in your data center.
This lecture will cover the techniques of Generative AI to generate new content based on various inputs. Recently, this has included using neural networks to identify patterns and structures in existing data and generate new content.
This course explores the biological and psychological background that inspired the world's first neural networks. The learning objectives are to explore how neural networks learn from data and understand the mathematical principles behind neurons. Through the course, participants will gain a deep understanding of how neural networks learn from data and the mathematical basis of neurons. To get the most out of this course, it is recommended that you have an understanding of basic programming concepts in Python 3 (functions, loops, dictionaries, arrays, etc.) and knowledge of how to compute regression lines.
This course will teach you how to make language models not just automation tools, but productive partners that can interact with various tools and documents to have informed conversations. Students will cover using embedding models, improving retrieval, setting up conversational safety devices, and implementing and modularizing augmented retrieval generation (RAG) agents that can explore research papers and provide answers without fine-tuning.
Augment your LLM Using Retrieval Augmented Generation
This lecture introduces methods to accelerate data workflows and aims to learn workflows that can save time required to make decisions about vector databases, embedding models, and large-scale language models (LLMs).
Jetson Nano can run multiple neural networks in parallel for applications such as image classification, object detection, segmentation, and speech processing. In this lecture, you will learn how to build a deep learning classification project using a computer vision model using a Jupyter iPython notebook on your Jetson Nano.
Building Video AI Applications at the Edge on Jetson Nano
This course introduces how to build AI-based video understanding using the NVIDIA Jetson Nano Developer Kit. You will experience the platform for introducing intelligent video analytics (IVA) applications for the first time using the NVIDIA DeepStream SDK.
Accelerate Data Science Workflows with Zero Code Changes
In this course, you will learn how to accelerate CPU-based data science workflows using RAPIDS. NVIDIA RAPIDS provides a seamless experience for GPU-accelerating many existing data science tasks without changing code. In this course, participants will learn how to use RAPIDS to accelerate CPU-based data science workflows.
NVIDIA’s educational offerings increase the understanding and accessibility of AI technologies, and give more people with technical literacy the opportunity to enter the world of programming. This is an important step toward lowering the barrier to technical education, even if the claim that “ anyone can be a programmer ” is somewhat exaggerated.
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