Complete the series of courses from DeepLearning.AI to

• Get familiar with core concepts, tools & frameworks
• Validate usage of Github Codespaces - for completing all exercises
• Skill up on using Jupyter notebooks in Codespaces - for JS & Python
• Explore use of GitHub Copilot Chat - for creating explainers & exercises
• Apply insights to a hack project - to validate learning in practice

Check out the notebooks/README for notes.

1. ChatGPT Prompt Engineering For Developers | 1 hr
2. Building Systems with the ChatGPT API | 1 hr
3. LangChain for LLM Application Development | 1 hr
4. LangChain: Chat with your Data) | 1 hr
5. Finetuning Large Language Models | 1 hr
6. Large Language Models with Semantic Search| 1 hr
7. Building Generative AI Applications with (HuggingFace) Gradio | 1 hr
8. Evaluating and Debugging Generative AI | 1 hr
9. How Diffusion Models Work | 1 hr
10. How Business Thinkers can start building AI Plugins with the Semantic Kernel | 1 hr
11. TBD | 1 hr

• Fundamentals of generative AI for real-world apps (with AWS)

• OpenAI Platform - Core Documentation
• ChatGPT - Text and Code Generation
• Dall-E - Image Genration
• Playground - Interactive Prompt Completions
• Developer Forum - Community Q&A

• Documentation
• Datasets - By Category (e.g. awesome-chatgpt-prompts)
• Models - By Category
• Spaces - Community Apps

• Llama 2 - next-gen open-source LLM
• Frameworks and Tools - PyTorch and more
• Models and Libraries - LLama, AudioCraft, and more
• Large-Scale Datasets

• Azure Open AI
• Semantic Kernel
• Azure AI Fundamentals - (1h 37 min)
• Develope Generative AI Solutions with Azure OpenAI (5h 34 min)
• Build AI Solutions with Prompt Flow - preview