Multimodal AI: Teaching Models to See, Hear, and Connect

The real world is never plain text — it is pixels, sound waves and words all at once. This course
teaches you to hand-build, in a few dozen lines of Python in your own sandbox, every mechanism
behind making one model see images, hear sound, and read text at the same time.
You start by turning all three modalities into tensors: an image is a pixel grid, audio is a 1-D
waveform and spectrum, text is a bag-of-words vector — seeing how each is natively shaped (that is
"heterogeneity"), then unifying them as vectors in one representation space.
Then you build alignment: pull paired (image, text) into the same space and use contrastive
learning (CLIP-style) to draw positives together and push negatives apart; build fusion — early,
late, gated, attention — and see which strategy is steadier on a toy bimodal task; build
cross-modal retrieval and transfer: image↔text search with recall@k, supervising one modality
with another's labels, and zero-shot transfer. From there you climb to large multimodal models
(projecting image features into "pseudo-tokens" fed to a toy language model), multimodal
generation, and evaluation. Everything runs offline, deterministically, in seconds, with every
image / sound / pair synthesized in code — so all your attention goes to the mechanism itself.