NVIDIA has jumped on the AI bandwagon in a big way. Hardware aside, this means working on training material too. Several self- paced courses have been released and for free too!

Nvidia once a company that exclusively catered for gamers by releasing state of the art GPUs, has now become an AI company. This happened as a side effect of the research they've been doing in their quest to produce ever evolving graphic cards. That lead them to the discovery of the tensor cores that are now used to train Large Language Models.

Demand for chips sky rocketed, stock prices went up and innovation reached new highs with the latest offering of the Blackwell AI chip, the next gen ancestor of the $40,000 H100 chips.

This AI hardware endeavor however has rubbed off onto Software too. Not long ago NVIDIA released ChatRTX, "Your Personalized AI Chatbot" that lets you personalize a GPT large language model (LLM) that connects to your own content - docs, notes, or other data. Leveraging retrieval-augmented generation (RAG), TensorRT-LLM, and RTX acceleration, you could query a custom chatbot to quickly get contextually relevant answers. And all of that running locally on your Windows RTX PC or workstation.

This was followed by the company's Chief's statement that everyone can now be a programmer thanks to the advent of generative AI. Ultimately you will be speaking at your computer which will blindly obey your commands without emitting compiler errors...

That might not happen in the near future but nevertheless, Nvidia has set out to educate and inform the public on what gen AI is and what it can do. Therefore as part of the Deep Learning Institute they offer a collection of courses, of which eight are self-paced as well as free. They are:

1. Generative AI Explained
In this no-coding course, you'll learn Generative AI concepts and applications, as well as the challenges and opportunities in this exciting field. Learning Objectives:

• Define Generative AI and explain how Generative AI works
• Describe various Generative AI applications
• Explain the challenges and opportunities in Generative AI

2. Getting Started with AI on Jetson Nano
Build and train a classification data set and model with the NVIDIA Jetson Nano. Learning Objectives:

• Set up your Jetson Nano and camera
• Collect image data for classification models
• Annotate image data for regression models
• Train a neural network on your data to create your models

3. Building A Brain in 10 Minutes
This notebook explores the biological inspiration for early neural networks. Learning Objectives:

• Explore how neural networks use data to learn
• Understand the math behind a neuron

4. Building Video AI Applications at the Edge on Jetson Nano
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.

5. Augment your LLM Using Retrieval Augmented Generation
In this introductory course, you’ll be provided with a high-level overview of Retrieval Augmented Generation and how it improves Generative AI (GenAI).

6. Building RAG Agents with LLMs
Agents powered by large language models (LLMs) have shown great retrieval capability for using tools, looking at documents, and plan their approaches. This course will show you how to deploy an agent system in practice with the flexibility to scale up your system to meet the demands of users and customers.

7. Accelerate Data Science Workflows with Zero Code Changes
In this course, you’ll learn to use RAPIDS to speed up your CPU-based data science workflows. Learning Objectives:

• 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.

8. Introduction to AI in the Data Center
In this course on Coursera, you'll learn about AI, machine learning, deep learning, GPU architecture, deep learning frameworks, and deploying AI workloads on Data Centers.

The courses span from 1 to 8 hours in duration and are addressed to technical beginners and intermediates.

From this list of courses which for instance teach how to build RAG agents, it is inferred that the "everyone can be a programmer" statement might be a bit overrated. In any case this is a first class opportunity to get into the world of GenAI.

More Information

1.Generative AI Explained

2.Getting Started with AI on Jetson Nano

3.Building A Brain in 10 Minutes

4.Building Video AI Applications at the Edge on Jetson Nano

5.Augment your LLM Using Retrieval Augmented Generation

6.Building RAG Agents with LLMs

7.Accelerate Data Science Workflows with Zero Code Changes

8.Introduction to AI in the Data Center

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