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DeepLearning.AI - Generative AI with Large Language Models 

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Generative AI with Large Language Models
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Coursera 
Overview

Duration

16 hours

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Total fee

Free

Mode of learning

Online

Difficulty level

Intermediate

Official Website

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Credential

Certificate

Generative AI with Large Language Models
 at 
Coursera 
Highlights

  • Flexible deadlines Reset deadlines in accordance to your schedule.
  • Shareable Certificate Earn a Certificate upon completion
  • 100% online Start instantly and learn at your own schedule.
  • Intermediate Level This is an intermediate course, so you should have some experience coding in Python to get the most out of it.
  • Approx. 16 hours to complete
  • English Subtitles: English
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Generative AI with Large Language Models
 at 
Coursera 
Course details

Skills you will learn
More about this course
  • In Generative AI with Large Language Models (LLMs), you will learn the fundamentals of how generative AI works, and how to deploy it in real-world applications.
  • By taking this course, you'll learn to:
  • - 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 are 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
  • Developers who have a good foundational understanding of how LLMs work, as well the best practices behind training and deploying them, will be able to make good decisions for their companies and more quickly build working prototypes. This course will support learners in building practical intuition about how to best utilize this exciting new technology.
  • 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 from DeepLearning.AI, you will be ready to take this course and dive deeper into the fundamentals of generative AI.
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Generative AI with Large Language Models
 at 
Coursera 
Curriculum

Week 1

Course Introduction

Introduction - Week 1

Generative AI & LLMs

LLM use cases and tasks

Text generation before transformers

Transformers architecture

Generating text with transformers

Prompting and prompt engineering

Generative configuration

Generative AI project lifecycle

Introduction to AWS labs

Lab 1 walkthrough

Pre-training large language models

Computational challenges of training LLMs

Optional video: Efficient multi-GPU compute strategies

Scaling laws and compute-optimal models

Pre-training for domain adaptation

Contributor Acknowledgments

Transformers: Attention is all you need

Domain-specific training: BloombergGPT

Week 1 resources

Lecture Notes Week 1

Week 2

Introduction - Week 2

Instruction fine-tuning

Fine-tuning on a single task

Multi-task instruction fine-tuning

Model evaluation

Benchmarks

Parameter efficient fine-tuning (PEFT)

PEFT techniques 1: LoRA

PEFT techniques 2: Soft prompts

Lab 2 walkthrough

Scaling instruct models

Week 2 Resources

Lecture Notes Week 2

Week 3

Introduction - Week 3

Aligning models with human values

Reinforcement learning from human feedback (RLHF)

RLHF: Obtaining feedback from humans

RLHF: Reward model

RLHF: Fine-tuning with reinforcement learning

Optional video: Proximal policy optimization

RLHF: Reward hacking

Scaling human feedback

Lab 3 walkthrough

Model optimizations for deployment

Generative AI Project Lifecycle Cheat Sheet

Using the LLM in applications

Interacting with external applications

Helping LLMs reason and plan with chain-of-thought

Program-aided language models (PAL)

ReAct: Combining reasoning and action

LLM application architectures

Optional video: AWS Sagemaker JumpStart

Responsible AI

Course conclusion

KL divergence

ReAct: Reasoning and action

Week 3 resources

Lecture Notes Week 3

Acknowledgments

(Optional) Opportunity to Mentor Other Learners

Generative AI with Large Language Models
 at 
Coursera 
Admission Process

    Important Dates

    May 25, 2024
    Course Commencement Date

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