Learn the fundamentals of generative AI for real-world applications
In Generative AI with Large Language Models (LLMs), created in partnership with AWS, you’ll learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.
Enroll nowIN COLLABORATION WITH
Learn the fundamentals of developing with LLMs
What you’ll get from Generative AI with LLMs
- Gain foundational knowledge, practical skills, and a functional understanding of how generative AI works
- Dive into the latest research on Gen AI to understand how companies are creating value with cutting-edge technology
- Instruction from expert AWS AI practitioners who actively build and deploy AI in business use-cases today
What you’ll do in Generative AI with LLMs
- Deeply understand generative AI, describing the key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection, to performance evaluation and deployment
- Describe in detail the transformer architecture that powers LLMs, how they’re trained, and how fine-tuning enables LLMs to be adapted to a variety of specific use cases
- Use empirical scaling laws to optimize the model’s objective function across dataset size, compute budget, and inference requirements
- Apply state-of-the art training, tuning, inference, tools, and deployment methods to maximize the performance of models within the specific constraints of your project
- Discuss the challenges and opportunities that generative AI creates for businesses after hearing stories from industry researchers and practitioners
- Receive a Coursera certificate demonstrating your skills upon completion of the course
In partnership with
We worked with AWS to develop a world-class AI course on large language models. Our instructors, with their extensive expertise in AI and machine learning, offer practical knowledge drawn from real-world experience that can be applied to your projects and career.
Who should join?
- For data scientists: Gain deeper knowledge into the underlying structure and mechanisms of generative AI and explore avenues for further innovations in this field.
- For machine learning engineers: Learn how to better train, optimize and fine tune generative models while learning about different use cases and applications.
- For prompt engineers: Explore advanced prompting techniques and learn how to control your output using generative configuration parameters.
- For research engineers: Explore the state of art generative models and architectures in depth to build on top of with your own advanced techniques in generative AI.
- For anyone interested in generative AI: Get an extensive introduction to developing with generative AI and its fundamentals.
Instructors
What do I need to succeed in this course?
This is an intermediate course, so you should have some experience coding in Python to get the most out of it. You should also be familiar with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets. If you have taken the Machine Learning Specialization or Deep Learning Specialization, you’ll be ready to take this course and dive deeper into the fundamentals of generative AI.
What Learners From Previous Courses Say About DeepLearning.AI
“Within a few minutes and a couple slides, I had the feeling that I could learn any concept. I felt like a superhero after this course. I didn’t know much about deep learning before, but I felt like I gained a strong foothold afterward.”
“The whole specialization was like a one-stop-shop for me to decode neural networks and understand the math and logic behind every variation of it. I can say neural networks are less of a black box for a lot of us after taking the course.”
“During my Amazon interview, I was able to describe, in detail, how a prediction model works, how to select the data, how to train the model, and the use cases in which this model could add value to the customer.”
Frequently Asked Questions
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