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addo561/README.md

Korli Larry Addo

Undergraduate · Spatial Intelligence · Building toward Large World Models

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Where I'm at

Undergraduate building from the ground up toward world model research — the kind of work that lets models understand, generate, and reason about persistent 3D environments. Starting with diffusion internals and moving toward spatial intelligence.

  • 🖼️  Currently: image diffusion — LDMs, SDEs, DiT architectures, built from scratch
  • 🎬  Next: video generation — temporal consistency, spacetime patching, scene persistence
  • 🧊  Then: 3D spatial generation — Gaussian Splatting, novel view synthesis, Score Distillation Sampling
  • 🌍  Goal: Large World Models — generative models that build and navigate coherent 3D worlds

Stack

Core ML

Python PyTorch NumPy scikit-learn Pandas OpenCV

Deployment & Tooling

FastAPI Docker Git


Trophies

GitHub trophies

Top Languages

Top languages


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  1. Conditional-Variational-AutoEncoder-cvae- Conditional-Variational-AutoEncoder-cvae- Public

    A PyTorch implementation of a Conditional Variational Autoencoder (CVAE) designed for controlled data generation.

    Jupyter Notebook

  2. DDIM-With-CFG DDIM-With-CFG Public

    DDIM with classifier free guidance

    Jupyter Notebook

  3. engine-2-transformer engine-2-transformer Public

    Transformer decoder built from scratch following Sebastien Raschka's approach — tokenization through to autoregressive text generation, no training frameworks.

    Jupyter Notebook

  4. mml-book.github.io mml-book.github.io Public

    Forked from mml-book/mml-book.github.io

    Companion webpage to the book "Mathematics For Machine Learning"

    Jupyter Notebook