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

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

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

A Visual Exploration of Gaussian Processes

Tübingen University - Probabilistic Machine Learning (2023)


Chris Olah

Marius Hobbhahn

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

Deep Implicit Layers

Hazy Research

Simon Coste

Ludwig Winkler

Fan Pu Zeng

Yang Song (宋飏)

Dive into Deep Learning

Patrick Kidger

On Neural Differential Equations

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

The Annotated Transformer

Thinking Like Transformers

Mamba: The Hard Way

Understanding Deep Learning

The Annotated Diffusion Model

On the Free Energy Principle

Machine Learning Trick of the Day (5): Log Derivative Trick

Monte Carlo Gradient Estimation in Machine Learning

Log-derivative trick

Hopfield Networks is All You Need

Michael Betancourt

Deep Learning Book (the famous Goodfellow one)


A detailed example of how to generate your data in parallel with PyTorch

Statistics 260: Lecture Notes (Michael I. Jordan)

Principal-Agent Hypothesis Testing

Prediction-Powered Inference

True Theta

Deep Learning (Chris Bishop)

Mark Huber - Probability Textbook

Stochastic Calculus, Filtering, and Stochastic Control

Terry Tao’s Blog

Andrew Gelman’s Blog - Statistical Modeling, Causal Inference, and Social Science

arg min

Career Advice

What You’ll Wish You’d Known

Terence Tao - A Close Call: How a Near Failure Propelled Me to Succeed