Multimodal Vision Systems

The chapters in this module, in reading order.

#	Chapter
00	Image & Video Models: Plain English First
01	Why dense pixels fail first — the network forgets the picture before it learns
02	Vision Transformers in plain sight — how small squares become visual tokens
03	CLIP and shared meaning — pull images and words into one coordinate system
04	Vision-Language Models — where seeing meets speaking
05	LLaVA and Frontier VLMs — the simple recipe behind smart image assistants
06	Training VLMs: Failure Points — where strong demos still crack
07	Image generation families — four ways to paint on the same canvas
08	Text-to-image pipeline — from prompt words to painted pixels
09	Image editing and control — steering the painter without repainting everything
10	Video tokenization and temporal modeling — time turns one picture into a crowd
11	Text-to-video systems — how moving clips are actually built
12	Production multimodal systems — reality begins when the bill arrives
13	Honest admission — what still feels unresolved in image and video models