Vision-Language Model

Build a ViT encoder, multimodal projector, causal decoder, training loop, and caption generation from raw tensor operations.

Hard5.2 hours62 steps

Overview

Build a ViT encoder, multimodal projector, causal decoder, training loop, and caption generation from raw tensor operations.

What you'll learn

Computer vision Multimodal fusion Generation

FULL WALKTHROUGH

Learn every step on its own page

This project is no longer compressed into a few chapters. Open the dedicated learning workspace for a lesson-by-lesson explanation with concepts, MathJax mathematics, code, tests, mistakes, checkpoints, and persistent navigation.

Open 62-step walkthrough →

Build progress

0 / 62 steps0%

Move the tracker as you finish the original Deep-ML steps. Reaching 100% unlocks the completion action and certificate.

Architecture

Work through the system one dependable layer at a time. Each stage feeds the next and remains independently testable.

01ViT→

02Projection layers→

03Autoregressive decoding

∑

Mathematics & visual explanation

Translate the core equations into code, validate intermediate tensors, and compare the implementation with a small numerical reference.

objective(θ) = data_term(θ) + λ · regularization(θ)

The exact objective evolves with each milestone. Keep a notebook of shapes, invariants, and numerical checks.