GitHub - aishwaryanr/awesome-generative-ai-guide: A one stop repository for generative AI research updates, interview resources, notebooks and much more!

⭐ 🔖 awesome-generative-ai-guide

Generative AI is experiencing rapid growth, and this repository serves as a comprehensive hub for updates on generative AI research, interview materials, notebooks, and more!

Explore the following resources:

We'll be updating this repository regularly, so keep an eye out for the latest additions!

Happy Learning!

⭐ Top AI Tools List

Discover our favorite AI tools spanning every layer of AI application development. Click here to learn more.

🔈 Announcements

NEW: AI Evals for Everyone course is now live with certification! (Click Here)
Applied LLMs Mastery full course content has been released!!! (Click Here)
5-day roadmap to learn LLM foundations out now! (Click Here)
60 Common GenAI Interview Questions out now! (Click Here)
ICLR 2024 paper summaries (Click Here)
List of free GenAI courses (Click Here)
Generative AI resources and roadmaps

🎓 Courses

[Ongoing] Applied LLMs Mastery 2024

Join 1000+ students on this 10-week adventure as we delve into the application of LLMs across a variety of use cases

Link to the course website

[Feb 2024] Registrations are still open click here to register

🗓️*Week 1 [Jan 15 2024]*: Practical Introduction to LLMs

Applied LLM Foundations
Real World LLM Use Cases
Domain and Task Adaptation Methods

🗓️*Week 2 [Jan 22 2024]*: Prompting and Prompt Engineering

Basic Prompting Principles
Types of Prompting
Applications, Risks and Advanced Prompting

🗓️*Week 3 [Jan 29 2024]*: LLM Fine-tuning

Basics of Fine-Tuning
Types of Fine-Tuning
Fine-Tuning Challenges

🗓️*Week 4 [Feb 5 2024]*: RAG (Retrieval-Augmented Generation)

Understanding the concept of RAG in LLMs
Key components of RAG
Advanced RAG Methods

🗓️*Week 5 [ Feb 12 2024]*: Tools for building LLM Apps

Fine-tuning Tools
RAG Tools
Tools for observability, prompting, serving, vector search etc.

🗓️*Week 6 [Feb 19 2024]*: Evaluation Techniques

Types of Evaluation
Common Evaluation Benchmarks
Common Metrics

🗓️*Week 7 [Feb 26 2024]*: Building Your Own LLM Application

Components of LLM application
Build your own LLM App end to end

🗓️*Week 8 [March 4 2024]*: Advanced Features and Deployment

LLM lifecycle and LLMOps
LLM Monitoring and Observability
Deployment strategies

🗓️*Week 9 [March 11 2024]*: Challenges with LLMs

Scaling Challenges
Behavioral Challenges
Future directions

🗓️*Week 10 [March 18 2024]*: Emerging Research Trends

Smaller and more performant models
Multimodal models
LLM Alignment

🗓️*Week 11 *Bonus* [March 25 2024]*: Foundations

Generative Models Foundations
Self-Attention and Transformers
Neural Networks for Language

📖 List of Free GenAI Courses

📎 Resources

ICLR 2024 Paper Summaries

💻 Interview Prep

Topic wise Questions:

Common GenAI Interview Questions
Prompting and Prompt Engineering
Model Fine-Tuning
Model Evaluation
MLOps for GenAI
Generative Models Foundations
Latest Research Trends

GenAI System Design (Coming Soon):

Designing an LLM-Powered Search Engine
Building a Customer Support Chatbot
Building a system for natural language interaction with your data.
Building an AI Co-pilot
Designing a Custom Chatbot for Q/A on Multimodal Data (Text, Images, Tables, CSV Files)
Building an Automated Product Description and Image Generation System for E-commerce

📓 Code Notebooks

RAG Tutorials

AWS Bedrock Workshop Tutorials by Amazon Web Services
Langchain Tutorials by gkamradt
LLM Applications for production by ray-project
LLM tutorials by Ollama
LLM Hub by mallahyari
RAG cookbook by CAMEL-AI

Fine-Tuning Tutorials

LLM Fine-tuning tutorials by ashishpatel26
PEFT example notebooks by Huggingface
Free LLM Fine-Tuning Notebooks by Youssef Hosni

Comprehensive LLM Code Repositories

LLM-PlayLab This playlab encompasses a multitude of projects crafted through the utilization of Transformer Models

✒️ Contributing

If you want to add to the repository or find any issues, please feel free to raise a PR and ensure correct placement within the relevant section or category.

📌 Cite Us

To cite this guide, use the below format:

@article{areganti_generative_ai_guide,
author = {Reganti, Aishwarya Naresh},
journal = {https://github.com/aishwaryanr/awesome-generative-ai-resources},
month = {01},
title = {{Generative AI Guide}},
year = {2024}
}

License

[MIT License]

^** This section is sponsored. We do not endorse or guarantee the product/service and are not responsible for any issues arising from its use. Please evaluate and use at your discretion.