Although we often overlook it, when we talk about Artificial Intelligence, the name of Nvidia is key to achieving all the advances that have been made so far, and the chip manufacturer has been one of the greats. benefited from the boom that this technology has had.
For that reason, Nvidia wants AI and all the technology that surrounds it to continue growing, which is going to happen, but to do so it needs users to adopt these advances in their daily lives. The main impediment to this is that people do not have training in AI and therefore do not know how to take advantage of it. For this reason, the American company has launched up to eight completely free courses so that you can begin training in this subject.
Free courses to train you in AI
Users will be able to learn from scratch and advance to better understand how to use AI and the different uses and applications it has. The only drawback is that most of them are only in English and that as you complete them, their difficulty increases. in terms of necessary knowledge.
Generative AI is used to generate new content based on a variety of inputs, primarily from text. In this course, you will learn the concepts and applications of Generative AI, as well as the challenges and opportunities of this field.
2. Introduction to AI in Jetson Nano
AI development is now in the hands of the NVIDIA Jetson Nano development kit, a powerful computer that allows you to run multiple neural networks in parallel for applications such as image classification, object detection, segmentation, and speech processing. In this course, you will build a deep learning classification project with computer vision models.
3. Build a brain in 10 minutes
This course is divided into two main objectives, on the one hand, to teach you how to use neural network data to learn, and to understand the mathematics behind a neuron.
4. Building Video AI Applications on Jetson Nano
This serves as an introduction to intelligent video analysis (IVA), and aims to teach you how to create DeepStream pipelines for video processing, manage multiple video streams, and use alternative inference engines like YOLO.
5. Improve your LLM with Recovery Augmented Generation (RAG)
Here you will learn to understand the fundamentals of RAG, learn about the RAG recovery process, and discover the NVIDIA AI Foundations and key components of a RAG model.
6. Accelerate data science workflows without code changes
In this course you will learn the benefits of unified CPU and GPU workflows, accelerate data processing and machine learning on the GPU, and see faster processing times with the GPU.
7. Introduction to AI in the data center
The main goal of this course is for you to learn about AI, machine learning, deep learning, GPU architecture, deep learning frameworks, and implementing AI workloads. Plus, it will also train you to understand the requirements of multi-system AI clusters and infrastructure planning.
Finally, with this course you learn how to create DeepStream applications to annotate video sequences using object detection and classification networks.
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