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This is an archive of my online notes on Computer Vision, Computer Graphics and Deep Learning!
Foundations of Physically Based Differentiable Rendering
Differentiable rendering techniques for computer graphics and vision
Introduction to 3D Rendering
A long-form guide to 3D rendering from first principles: transforms, camera and projection, ray generation, intersections, shading, ray tracing, path tracing, signed distance functions, and volume rendering.
A comprehensive look into the Importance Sampling and Path Guiding for Path Tracing
Exploring the Importance Sampling Techniques to reduce variance in Monte Carlo Path Tracing
3D Reconstruction with 3DGS, NeRF, and Their Variants
An in-depth look at modern 3D reconstruction methods such as 3DGS and NeRF, comparing their architectures, strengths, and performance in diverse applications.
Bagging and Boosting
Explore bagging and boosting in machine learning, including variance reduction, bias management, and ensemble classifier construction
Bias–Variance Tradeoff
Understanding how bias and variance affect model performance
Vision Transformer (ViT) - Architecture and Implementation
Understanding how transformers use scaled dot-product attention with multiple heads to process sequential data efficiently
Linear Regression with MLE and MAP
Linear Regression using MLE and MAP with visualization and implementation
Parameter Estimation: From MLE to Full Bayesian Inference
Maximum Likelihood, MAP and Bayesian Inference for probability estimation
Dimensionality Reduction using PCA
Notes on the minimum reconstruction error interpretation of PCA and high dimensional PCA