Course Overview
This comprehensive course covers the fundamental concepts and architectures behind modern generative AI systems. From transformer architectures to diffusion models, you'll gain deep understanding of how these systems work.
Transformer Architecture
Deep dive into attention mechanisms, encoder-decoder structures, and the innovations that made modern LLMs possible.
Large Language Models
Understanding GPT, Claude, Llama and other LLM architectures. Training paradigms, tokenization, and scaling laws.
Prompt Engineering
Master the art of crafting effective prompts. Few-shot learning, chain-of-thought, and advanced prompting techniques.
Diffusion Models
How image generation works. Understanding DALL-E, Midjourney, Stable Diffusion and the diffusion process.
Hands-On Exercises
- Implement attention mechanism from scratch in PyTorch
- Fine-tune a small transformer on custom data
- Build a prompt engineering framework with templates
- Create a comparison of different LLM APIs
- Generate images with Stable Diffusion and analyze the process
- Implement chain-of-thought prompting for complex reasoning
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