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Generative AI has become mainstream in recent months and will continue to improve. So how can you improve your skills and stay up to date with recent advances?
But here's the good news. With recent advances, the number of high-quality free learning resources available has also increased. This is a collection of free AI courses from NVIDIA (NVIDIA Deep Learning Institute) to help you understand AI topics and start building impactful solutions.
Now, let's take a look at the course and its contents.
Generative AI explanation
Generative AI Explained is a ready-to-use introduction to the basics of generative AI for beginners. This course covers the following topics:
- Generative AI and how it works
- Generative AI applications
- Challenges and opportunities in generative AI
By the end of this course, you will have a good understanding of what generative AI is, how it works, and how it can be used.
Link: Generation AI explanation
Train your brain in 10 minutes
Large-scale language models are currently very popular and extremely useful. However, before we dive into LLM, we need a basic understanding of how neural networks work.
Building a Brain in 10 Minutes is an introduction to building neural networks, with biological inspiration to guide the architecture of neural networks.
To get the most out of this course, you should be comfortable programming with Python and regression models. This short course will help you learn:
- How neural networks learn from data
- The mathematics behind neurons and how neural networks work
Link: Train your brain in 10 minutes
Extend LLM using search extension generation
When building applications that use LLM, you also use search extension generation (RAG). RAG allows you to build LLM apps based on domain-specific data and reduce LLM hallucinations.
The “Extending LLM with Search Enhancement Generation'' course shows you how to build RAG pipelines that use information retrieval and response generation. It will help you better understand the basics of RAG and the RAG acquisition process.
Link: Extending LLM using search extension generation
Building a RAG agent using LLM
Once you understand how RAGs work in the previous course, you can explore RAGs further by building an end-to-end LLM system by taking the Building RAG Agents with LLM course.
To complete this course, it is helpful to have intermediate programming experience with Python and some experience programming with PyTorch. This course explores LLM pipeline design and uses tools such as Gradio, LangChain, and LangServe. You can also experiment with vector stores for embedding, models, and retrieval.
Link: Building a RAG agent using LLM
summary
We hope you find this comprehensive list of free AI courses from NVIDIA Deep Learning Institute helpful.
However, if you are interested in learning more about LLM and generative AI, here are some articles that may be helpful.
Have fun learning and coding!
Rose Priya C I'm a developer and technical writer from India. She likes working at the intersection of math, programming, data science, and content creation. Her interests and expertise include DevOps, data science, and natural language processing. She loves reading, writing, coding, and coffee. Currently, she is committed to learning and sharing her knowledge with the developer community by creating tutorials, how-to guides, opinion articles, and more. Bala also creates engaging resource summaries and coding tutorials for her.
