Learn to build a full 3D Gaussian Splatting (3DGS) pipeline entirely in Python and PyTorch — no CUDA, no external libraries. This course walks you through every step of modern neural rendering: parsing COLMAP outputs, implementing 3D Gaussian primitives, creating a trainable radiance model, coding the optimization loop, and building a real-time differentiable renderer for photorealistic results. Ideal for researchers, graduate students, and developers who want a clean, modular PyTorch implementation and a deep understanding of 3DGS without C++ or CUDA. After 7k training iterations, your metrics will match those reported in the original paper. By the end, you’ll have a complete working implementation ready for extension and research use.