davidrmh

reinforcement-learning-an-introduction

Python Implementation of Reinforcement Learning: An Introduction

the-incredible-pytorch

The Incredible PyTorch: a curated list of tutorials, papers, projects, communities and more relating to PyTorch.

smallchat

A minimal programming example for a chat server

awesome-systematic-trading

A curated list of awesome libraries, packages, strategies, books, blogs, tutorials for systematic trading.

adv-r

Advanced R: a book

PhySO

Physical Symbolic Optimization

cvxpylayers

Differentiable convex optimization layers

awesome-openai-vision-api-experiments

Must-have resource for anyone who wants to experiment with and build on the OpenAI vision API 🔥

PythonNumericalDemos

Well-documented Python demonstrations for spatial data analytics, geostatistical and machine learning to support my courses.

Essentials-of-Compilation

A book about compiling Racket and Python to x86-64 assembly

RustQuant

Rust library for quantitative finance.

LeanCopilot

LLMs as Copilots for Theorem Proving in Lean

databook_python

IPython notebooks with demo code intended as a companion to the book "Data-Driven Science and Engineering: Machine Learning, Dynamical Systems, and Control" by Steven L. Brunton and J. Nathan Kutz

uncertain_ground_truth

Dermatology ddx dataset, Jax implementations of Monte Carlo conformal prediction, plausibility regions and statistical annotation aggregation from our recent work on uncertain ground truth (TMLR'23 and ArXiv pre-print).

AlgorithmsAndDataStructuresInAction

Advanced Data Structures Implementation

mbmlbook

Sample code for the Model-Based Machine Learning book.

tutorials

This is the official repository for the "doggo dot jl" YouTube channel (fka "julia for talented amateurs").

neuraluq

book

Code for the book "Mathematical Introduction to Deep Learning: Methods, Implementations, and Theory"

CppCon2021

Slides and other materials from CppCon2021

geometry-of-truth

Bayesian-Neural-Networks-Reading-List

A primer on Bayesian Neural Networks. The aim of this reading list is to facilitate the entry of new researchers into the field of Bayesian Deep Learning, by providing an overview of key papers. More details: "A Primer on Bayesian Neural Networks: Review and Debates"

Python-software-engineering

Repository for participants of the "Python software engineering" training

odemodeling

R-package for building and fitting Bayesian ODE models in Stan

VolGAN

Code to accompany the paper "VolGAN: a generative model for arbitrage-free implied volatility surfaces"

pic

Official implementation of Probabilistic Integral Circuits

alpha-MDF

Codebase for α-MDF at CoRL 2023

intro-gnn

A gentle introduction to Graph Neural Networks in Pytorch Geometric

reinforcement_learning_an_introduction

Python Implementation for problems in Reinforcement Learning - An Introduction book

L-RVGA

The companion code for the article "The limited-memory recursive variational Gaussian approximation. (L-RVGA), M. Lambert, S. Bonnabel, F. Bach"