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

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)

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

Lenia (A system of continuous cellular automata)

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)

Convex Optimization for Trajectory Generation

CS 285 at UC Berkeley - Deep Reinforcement Learning