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
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