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

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)

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

On Neural Differential Equations

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

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

Monte Carlo Gradient Estimation in Machine Learning

Hopfield Networks is All You Need

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

Mark Huber - Probability Textbook

Stochastic Calculus, Filtering, and Stochastic Control

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

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