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DeepLearning.AI

Generative AI with LLMs

  • up to 1 month
  • Intermediate

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. Gain foundational knowledge, practical skills, and a functional understanding of how generative AI works.

  • Generative AI
  • Large Language Models
  • Transformer Architecture
  • Model Training
  • Model Fine-Tuning

Overview

This course provides a comprehensive understanding of generative AI, covering key steps in a typical LLM-based generative AI lifecycle, from data gathering and model selection to performance evaluation and deployment. You will learn about the transformer architecture, empirical scaling laws, and state-of-the-art training, tuning, inference, tools, and deployment methods. The course also discusses the challenges and opportunities that generative AI creates for businesses, with insights from industry researchers and practitioners.

  • Web Streamline Icon: https://streamlinehq.com
    Online
    course location
  • Layers 1 Streamline Icon: https://streamlinehq.com
    English
    course language
  • Professional Certification
    upon course completion
  • Self-paced
    course format
  • Live classes
    delivered online

Who is this course for?

Data Scientists

Gain deeper knowledge into the underlying structure and mechanisms of generative AI and explore avenues for further innovations in this field.

Machine Learning Engineers

Learn how to better train, optimize and fine-tune generative models while learning about different use cases and applications.

Prompt Engineers

Explore advanced prompting techniques and learn how to control your output using generative configuration parameters.

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.

Anyone interested in generative AI

Get an extensive introduction to developing with generative AI and its fundamentals.

Gain foundational knowledge, practical skills, and a functional understanding of how generative AI works. Learn from expert AWS AI practitioners and apply the latest research and techniques to real-world applications. Ideal for data scientists, machine learning engineers, prompt engineers, research engineers, and anyone interested in generative AI.

Pre-Requisites

1 / 2

  • Experience coding in Python

  • Familiarity with the basics of machine learning, such as supervised and unsupervised learning, loss functions, and splitting data into training, validation, and test sets

What will you learn?

Introduction to Generative AI
Learn the basics of generative AI and its applications in various industries.
Understanding Large Language Models
Dive into the structure and mechanisms of large language models (LLMs).
Transformer Architecture
Explore the transformer architecture that powers LLMs and understand how they are trained.
Model Training and Fine-Tuning
Learn how to train and fine-tune generative models for specific use cases.
Empirical Scaling Laws
Use empirical scaling laws to optimize the model’s objective function across dataset size, compute budget, and inference requirements.
Model Deployment
Apply state-of-the-art deployment methods to maximize the performance of models within the specific constraints of your project.
AI Business Applications
Discuss the challenges and opportunities that generative AI creates for businesses.

What learners say about this course

  • “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.”

    Jan Zawadzki

    Data Scientist at Carmeq

  • “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.”

    Kritika Jalan

    Data Scientist at Corecompete Pvt. Ltd.

  • “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.”

    Chris Morrow

    Sr. Product Manager at Amazon

Meet your instructors

  • Antje Barth

    Principal Developer Advocate, Generative AI, Amazon Web Services (AWS)

    Antje Barth is a Principal Developer Advocate for generative AI at Amazon Web Services, helping developers build with generative AI. She co-authored the O'Reilly book "Generative AI on AWS" and created the "Generative AI with large language models" course with DeepLearning.AI.

  • Chris Fregly

    Lead Instructor and Developer, DeepLearning.AI

    Chris Fregly is a Lead Instructor and Developer at DeepLearning.AI, where he helps learners gain foundational knowledge and practical skills in generative AI.

  • Shelbee Eigenbrode

    AI/ML Specialist Solutions Architect, Amazon Web Services (AWS)

    Shelbee Eigenbrode is passionate about AWS as a strategic cloud platform and using AI/ML to solve business problems driving quantifiable outcomes. She is currently focusing a lot of her time on applying DevOps practices to Machine Learning workloads (MLOps) to enable customers to adopt Machine Learning at scale.

  • Mike Chambers

    Specialist Developer Advocate, Machine Learning, Amazon Web Services

    Mike Chambers is an experienced cloud, serverless and machine learning trainer and evangelist. He has been a Specialist Developer Advocate for Machine Learning at Amazon Web Services for some time.

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