Welcome! 👋

This is an archive of my online notes on Computer Vision, Computer Graphics and Deep Learning!

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