Module 12 – Multimodal Learning & GenAI
What you'll learn
Builds on Modules 1–11
≈8 h · 2.5 h video · 3 h reading · 2.5 h coding
- Explain symmetric image–text contrastive learning and audit cross-modal retrieval
- Implement a tiny VAE; explore the latent space qualitatively
- Train a small GAN; apply simple stability tweaks; assess samples
- Explain forward/reverse diffusion and demonstrate sampling
- Briefly discuss evaluation metrics and ethical considerations
12.1 – Multimodal Learning
Vision-language alignment, contrastive objectives (e.g., CLIP), and fusion strategies for building multimodal systems.
Slide preview
12.2 – Diffusion Models
Noise schedules, forward/reverse processes, and sampling recipes that power modern generative diffusion pipelines.
Slide preview
12.3 – Variational Autoencoders (VAE)
Latent variable modeling with encoder/decoder pairs, ELBO optimization, and practical VAE architectures.
Slide preview
12.4 – Generative Adversarial Networks (GAN)
Adversarial training, loss variants, and practical tips for stabilizing GANs for image generation.
Slide preview
📚 Resources & Lecture Code
Required reading · course book
End of course