conalldaly.com

Trying to gather some of the tools and resources I find useful/want to explore:

Signal Processing

Introduction to Probability, Statistics, and Random Processes by Hossein Pishro-Nik

Probability Theory I

Michiel Stock - Notes on Optimal Transport

Autoregressive Processes are Gaussian Processes

Kalman and Bayesian Filters in Python

Multi Object Tracking in Python

Random Processes for Engineers

MIT 6.262 Discrete Stochastic Processes

Lánczos interpolation explained

The Variational Approximation for Bayesian Inference: Life after the EM algorithm

A Visual Exploration of Gaussian Processes

The free-energy principle: a unified brain theory?

Designing Ecosystems of Intelligence from First Principles

On Bayesian Mechanics: A Physics of and by Beliefs

Tübingen University - Probabilistic Machine Learning (2023)

Lil’Log

Chris Olah

Marius Hobbhahn

Separate your filters! Separability, SVD and low-rank approximation of 2D image processing filters

An Introduction to Neural Data Compression

Hacker’s Guide to Neural Networks

Convolutional Neural Networks for Visual Recognition

A guide to convolution arithmetic for deep learning

The Annotated S4: Efficiently Modeling Long Sequences with Structured State Spaces

Hazy Research

Software

Full Stack Python

High Performance Browser Networking

Algorithms for Modern Hardware

Modern-CPP-Programming

How to Write a Video Player in Less Than 1000 Lines (with some help from FFmpeg)

Modern x64 Assembly

What Every Programmer Should Know About Memory - Ulrich Drepper

MIT 6.172 Performance Engineering of Software Systems

Introduction to High-Performance Scientific Computing

LAFF-On Programming for High Performance

The Valgrind Quick Start Guide

Introduction to OpenMP - Tim Mattson (Intel)

Intel Intrinsics Guide

Build Systems à la Carte

Advanced Linear Algebra: Foundations and Frontiers

CUDA C Programming Guide

How to Optimize a CUDA Matmul Kernel for cuBLAS-like Performance: a Worklog

An Even Easier Introduction to CUDA

Ronald Bultje Blog

Graphics

Monte Carlo Methods and Applications

Robust Monte Carlo Methods for Light Transport Simulation

What Makes a (Graphics) Systems Paper Beautiful

Physically Based Rendering

Nathan Reed

How to Read a Realistic Rendering Paper

Parallelizing Ray Tracer in a Weekend

Why Geometry Shaders Are Slow (Unless you’re Intel)

Ray Tracing in One Weekend

Accelerated Ray Tracing in One Weekend in CUDA

Physics-Based Differentiable and Inverse Rendering