Create images, video, audio, 3D, and more.
Start this phase on GitHub
Prerequisites: Phase 2 ML Fundamentals, Phase 3 Deep Learning Core, and Phase 7 Lesson 14, Build a Transformer from Scratch.
First lesson: Generative Model Taxonomy and History
Run this command from the repository root:
bash
python3 phases/08-generative-ai/01-generative-models-taxonomy-history/code/main.pyKeep the command, exit code, density estimates, generated samples, and one
sentence explaining what an implicit generator cannot answer about p(x).
Next action: Change the random seed, compare the density estimates, then continue to Autoencoders and VAE.
Browse the full Phase 8 lesson list or the cross-phase roadmap.
15 lessons, about 15 hours total. Each lesson ships a detailed document, a runnable Python demo, a diagram, and a named skill for your agent.
| # | Lesson | Time |
|---|---|---|
| 01 | Generative Models: Taxonomy and History | ~45 min |
| 02 | Autoencoders & VAE | ~75 min |
| 03 | GANs: Generator vs Discriminator | ~75 min |
| 04 | Conditional GANs & Pix2Pix | ~75 min |
| 05 | StyleGAN | ~45 min |
| 06 | Diffusion Models: DDPM from Scratch | ~75 min |
| 07 | Latent Diffusion & Stable Diffusion | ~75 min |
| 08 | ControlNet, LoRA & Conditioning | ~75 min |
| 09 | Inpainting, Outpainting & Editing | ~75 min |
| 10 | Video Generation | ~45 min |
| 11 | Audio Generation | ~45 min |
| 12 | 3D Generation | ~45 min |
| 13 | Flow Matching & Rectified Flows | ~45 min |
| 14 | Evaluation: FID, CLIP Score, Human Preference | ~45 min |
| 19 | Visual Autoregressive Modeling | ~60 min |