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Generative ai has become commonplace in recent months and it's only going to get better. So how can you upskill and keep up with all the recent advancements?
But here's the good news: with all the recent advances there has also been an increase in the number of high-quality free learning resources available. This is a collection of free ai courses from NVIDIA (the NVIDIA Deep Learning Institute) to get you up to speed on ai topics and start creating impactful solutions.
So let's review the courses and what they cover!
Generative ai explained
Generative ai explained is a beginner's introduction to the fundamentals of generative ai to get you familiar. This course will introduce you to the following topics:
- Generative ai and how generative ai works
- Generative ai applications
- Challenges and opportunities in generative ai
By the end of this course, you will have gained a good understanding of what generative ai is, how it works, and how you can use it.
Link: Generative ai explained
Building a brain in 10 minutes
Large language models are currently very popular and very useful. However, before diving into LLMs, it is necessary to have a basic understanding of how neural networks work.
Building a brain in 10 minutes is an introduction to building a neural network with references to biological inspirations that guide the architecture of the neural network.
To get the most out of this course, you should be comfortable with Python programming and regression models. This short course will help you learn the following:
- How neural networks learn from data
- The mathematics behind the neuron and how a neural network works
Link: Building a brain in 10 minutes
Increase your LLM using increased recovery generation
Whenever you want to build applications that use LLM, you will also use Recovery Augmented Generation (RAG). With RAG, you can build LLM applications from domain-specific data, mitigate LLM hallucinations, and much more.
He Increase your LLM using increased recovery generation The course will teach you how to build a RAG pipeline that uses information retrieval and response generation. It will help you to have a good understanding of RAG basics and RAG recovery process.
Link: Increase your LLM using increased recovery generation
Creating RAG agents with LLM
Once you are familiar with how RAG works from the previous course, you can take the Creating RAG agents with LLM course to explore RAG in much more detail by creating end-to-end LLM systems.
To pass this course, it will be helpful to have intermediate programming experience with Python and some programming experience with PyTorch. In this course, you will explore LLM process design and use tools such as Built, LangChainand LangServe. You can also experiment with embeddings, models, and vector stores for retrieval.
Link: Creating RAG agents with LLM
Ending
I hope you found this comprehensive list of free ai courses from the NVIDIA Deep Learning Institute helpful.
But if you are interested in exploring LLMs and generative ai further, here are a couple of articles that you may find useful:
Happy learning and coding!
twitter.com/balawc27″ rel=”noopener”>Bala Priya C. is a developer and technical writer from India. He enjoys working at the intersection of mathematics, programming, data science, and content creation. His areas of interest and expertise include DevOps, data science, and natural language processing. He likes to read, write, code and drink coffee! Currently, he is working to learn and share his knowledge with the developer community by creating tutorials, how-to guides, opinion pieces, and more. Bala also creates engaging resource descriptions and coding tutorials.
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