Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/quang-pham-dev/generative-ai-with-large-language-models

Learn Generative AI with Large Language Models (LLMs)
https://github.com/quang-pham-dev/generative-ai-with-large-language-models

deep-learning generative-ai large-language-models python-3

Last synced: 12 days ago
JSON representation

Learn Generative AI with Large Language Models (LLMs)

Awesome Lists containing this project

README 

        
# [Generative AI with LLMs](https://www.coursera.org/learn/generative-ai-with-llms)



This comprehensive course provides a deep dive into Generative AI and Large Language Models (LLMs), equipping you with practical knowledge and skills to leverage this transformative technology.



## Course Highlights



- **Foundational Knowledge**: Gain a thorough understanding of generative AI fundamentals, including model architectures, training methodologies, and deployment strategies.



- **Practical Skills**: Learn hands-on techniques for training, fine-tuning, and deploying LLMs in real-world applications.



- **Industry Expertise**: Learn directly from AWS AI practitioners who actively build and deploy AI solutions for business use-cases.



- **Cutting-Edge Research**: Stay current with the latest developments in Generative AI and understand how companies are creating value with this technology.



## Course Content



### Week 1: Generative AI use cases, project lifecycle, and model pre-training



#### Videos (116 minutes total)



- Course Introduction (6 min)

- Introduction to Week 1 (5 min)

- Generative AI & LLMs (4 min)

- LLM use cases and tasks (2 min)

- Text generation before transformers (2 min)

- Transformers architecture (7 min)

- Generating text with transformers (5 min)

- Prompting and prompt engineering (5 min)

- Generative configuration (7 min)

- Generative AI project lifecycle (4 min)

- Introduction to AWS labs (5 min)

- Lab 1 walkthrough (14 min)

- Pre-training large language models (9 min)

- Computational challenges of training LLMs (10 min)

- Optional: Efficient multi-GPU compute strategies (8 min)

- Scaling laws and compute-optimal models (8 min)

- Pre-training for domain adaptation (5 min)



#### Readings (52 minutes total)



- Contributor Acknowledgments (10 min)

- Forum Guidelines and Information (2 min)

- Transformers: Attention is all you need (10 min)

- Lab Guidelines (5 min)

- Domain-specific training: BloombergGPT (10 min)

- Week 1 resources (10 min)

- Lecture Notes Week 1 (5 min)



#### Hands-on Learning



- [Week 1 Assessment](./Week-1/Week_1_quiz.md) (60 min)

- [Lab: Generative AI Use Case - Dialogue Summarization](./Week-1/Lab_1_summarize_dialogue.ipynb) (120 min)

- Intake Survey (1 min)



### Week 2: Fine-tuning and evaluating large language models



#### Videos (77 minutes total)



- Introduction - Week 2 (4 min)

- Instruction fine-tuning (7 min)

- Fine-tuning on a single task (3 min)

- Multi-task instruction fine-tuning (8 min)

- Model evaluation (10 min)

- Benchmarks (5 min)

- Parameter efficient fine-tuning (PEFT) (4 min)

- PEFT techniques 1: LoRA (8 min)

- PEFT techniques 2: Soft prompts (7 min)

- Lab 2 walkthrough (17 min)



#### Readings (25 minutes total)



- Scaling instruct models (10 min)

- Week 2 Resources (10 min)

- Lecture Notes Week 2 (5 min)



#### Hands-on Learning



- [Week 2 Assessment](./Week-2/Week_2_quiz.md) (60 min)

- [Lab: Fine-tune a generative AI model for dialogue summarization](./Week-2/Lab_2_fine_tune_generative_ai_model.ipynb) (120 min)



### Week 3: Reinforcement Learning and LLM-Powered Applications



#### Videos (141 minutes total)



- Introduction - Week 3 (4 min)

- Aligning models with human values (3 min)

- Reinforcement learning from human feedback (RLHF) (8 min)

- RLHF: Obtaining feedback from humans (6 min)

- RLHF: Reward model (2 min)

- RLHF: Fine-tuning with reinforcement learning (3 min)

- Optional video: Proximal policy optimization (13 min)

- RLHF: Reward hacking (6 min)

- Scaling human feedback (5 min)

- Lab 3 walkthrough (18 min)

- Model optimizations for deployment (7 min)

- Generative AI Project Lifecycle Cheat Sheet (2 min)

- Using the LLM in applications (9 min)

- Interacting with external applications (4 min)

- Helping LLMs reason and plan with chain-of-thought (5 min)

- Program-aided language models (PAL) (7 min)

- ReAct: Combining reasoning and action (9 min)

- LLM application architectures (5 min)

- Optional video: AWS Sagemaker JumpStart (5 min)

- Responsible AI (9 min)

- Course conclusion (3 min)



#### Readings (43 minutes total)



- KL divergence (10 min)

- [IMPORTANT] Reminder about end of access to Lab Notebooks (2 min)

- ReAct: Reasoning and action (10 min)

- Week 3 resources (10 min)

- Lecture Notes Week 3 (5 min)

- Acknowledgments (1 min)

- (Optional) Opportunity to Mentor Other Learners (5 min)



#### Hands-on Learning



- [Week 3 Assessment](./Week-3/Week_3_quiz.md) (60 min)

- [Lab: Fine-tune FLAN-T5 with reinforcement learning to generate more-positive summaries](./Week-3/Lab_3_fine_tune_model_to_detoxify_summaries.ipynb) (120 min)

- Reinforcement Learning from Human Feedback (RLHF)

- Chain-of-thought prompting

- Knowledge augmentation techniques

- Real-world application strategies



## Course Completion & Certification



After completing all the labs, quizzes, and assessments across the three weeks, you will receive a verified certificate from Coursera demonstrating your proficiency in Generative AI and LLMs. This certificate validates your understanding of:



- Foundational concepts in Generative AI

- Advanced model training and fine-tuning techniques

- Practical application of LLMs in real-world scenarios



View my course completion certificate [here](./certificate.png)