Normalizing flows in PyTorch

Chat with GitHub Copilot in Neovim

Watch tutorial: https://youtu.be/jJVAla0dWJo

Fastify 5 application boilerplate based on clean architecture, domain-driven design, CQRS, functional programming, vertical slice architecture for building production-grade applications 🚀

Learn Domain-Driven Design, software architecture, design patterns, best practices. Code examples included

Playwright is a framework for Web Testing and Automation. It allows testing Chromium, Firefox and WebKit with a single API.

FastAPI Best Practices and Conventions we used at our startup

A fancy self-hosted monitoring tool

A collection of resources regarding the interplay between differential equations, deep learning, dynamical systems, control and numerical methods.

Code repo for "A Simple Baseline for Bayesian Uncertainty in Deep Learning"

Acceptance rates for the major AI conferences

UQpy (Uncertainty Quantification with python) is a general purpose Python toolbox for modeling uncertainty in physical and mathematical systems.

An authorization library that supports access control models like ACL, RBAC, ABAC in Golang: https://discord.gg/S5UjpzGZjN

CASL is an isomorphic authorization JavaScript library which restricts what resources a given user is allowed to access

Generate your custom print-ready Dobble Game

Cluster IALAB. Documentación, scripts, archivos de configuración.

Open-source framework for uncertainty and deep learning models in PyTorch 🌱

👋 Puncc is a python library for predictive uncertainty quantification using conformal prediction.

Code and ressources for the article Large Language Models Behave (Almost) As Rational Speech Actors

A simple and extensible library to create Bayesian Neural Network layers on PyTorch.

Refine high-quality datasets and visual AI models

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Deep universal probabilistic programming with Python and PyTorch

Monte is a set of Monte Carlo methods in Python. The package is written to be flexible, clear to understand and encompass variety of Monte Carlo methods.

This repo contains a PyTorch implementation of the paper: "Evidential Deep Learning to Quantify Classification Uncertainty"

Bayesian Deep Learning Benchmarks

" Weight Uncertainty in Neural Networks"