Name: Dirk Roeckmann
Type: User
Bio: Comp. Scientist, IT Manager, Consultant, SAP Expert,
Indep. AI Researcher,
AI = Deep Learning + Causal Inference + Symbol Manipulation, Deep Quadric Learning
Twitter: fivetroop
Location: Pittsburgh
Dirk Roeckmann's Projects
BAyesian Model-Building Interface (Bambi) in Python.
Causal Inference and Discovery in Python by Packt Publishing
Causal Discovery in Python. It also includes (conditional) independence tests and score functions.
Notes, exercises and other materials related to causal inference, causal discovery and causal ML.
the AI-native open-source embedding database
Deep Quadric Learning
DeepProbLog is an extension of ProbLog that integrates Probabilistic Logic Programming with deep learning by introducing the neural predicate.
[Experimental] Global causal discovery algorithms
DoWhy is a Python library for causal inference that supports explicit modeling and testing of causal assumptions. DoWhy is based on a unified language for causal inference, combining causal graphical models and potential outcomes frameworks.
ALICE (Automated Learning and Intelligence for Causation and Economics) is a Microsoft Research project aimed at applying Artificial Intelligence concepts to economic decision making. One of its goals is to build a toolkit that combines state-of-the-art machine learning techniques
This repository describes the governance model for the PyWhy org
JupyterLab desktop application, based on Electron.
Deep Learning for humans
Keras documentation, hosted live at keras.io
🦜🔗 Build context-aware reasoning applications
Get up and running with Llama 3, Mistral, Gemma, and other large language models.
Ollama Python library
ProbLog is a Probabilistic Logic Programming Language for logic programs with probabilities.
Bayesian Modeling and Probabilistic Programming in Python
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Experimental library integrating LLM capabilities to support causal analyses
Keep track of discussions and meeting minutes.
Python package for (conditional) independence testing and statistical functions related to causality.
Official Stanford NLP Python Library for Many Human Languages
You like pytorch? You like micrograd? You love tinygrad! ❤️