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diamond

DIAMOND (DIffusion As a Model Of eNvironment Dreams) is a reinforcement learning agent trained in a diffusion world model.

dice

Generate Diverse Counterfactual Explanations for any machine learning model.

diffusers

🤗 Diffusers: State-of-the-art diffusion models for image and audio generation in PyTorch

digirl

Official repo for paper DigiRL: Training In-The-Wild Device-Control Agents with Autonomous Reinforcement Learning.

digital_champions_deeplearning_r_mxnet

Showcase for using R + MXNET along with AWS and bitfusion for deep learning.

digits

Deep GPU Training System

dissertacao

projeto de dissertação com o Numerai Tournament

dit

Official PyTorch Implementation of "Scalable Diffusion Models with Transformers"

dl-machine

Scripts to setup a GPU / CUDA-enabled compute server with libraries for deep learning

dl_study_group

MLT Study Group resources: Deep Learning

dmd

Python implementation of Dynamic Mode Decomposition (DMD) and Multi-Resolution Dynamic Mode Decomposition (mrDMD) algorithms

dmtk

Microsoft Distributed Machine Learning Tookit

doctorgpt

DoctorGPT is an LLM that can pass the US Medical Licensing Exam. It works offline, it's cross-platform, & your health data stays private.

dolly

Databricks’ Dolly, a large language model trained on the Databricks Machine Learning Platform

dowhy

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.

dp_notebooks

dqn

This is a very basic DQN implementation, which uses OpenAI's gym environment and Keras/Theano neural networks.

dqn-in-the-caffe

An implementation of Deep Q-Network using Caffe

drawio

Source to app.diagrams.net

dreamer

Dream to Control: Learning Behaviors by Latent Imagination

dsb2

dsc-complex-time-series-challenge

dspy

DSPy: The framework for programming with foundation models

dte

[ATENCIÓN: A partir del 1º Enero 2017 ya no realizaremos mantención de este proyecto]. Bibliotecas de factura electrónica (DTE), desarrollada y mantenida por Jose Urzua y NIC Chile Research Labs

dueldao-chess-contract

dynamics-and-control

Jupyter notebooks for Dynamics and Control

eattheblocks

Source code for Eat The Blocks, a screencast for Ethereum Dapp Developers

ecj

Automatically exported from code.google.com/p/ecj

econml

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 with econometrics in order to bring automation to complex causal inference problems. To date, the ALICE Python SDK (econml) implements orthogonal machine learning algorithms such as the double machine learning work of Chernozhukov et al. This toolkit is designed to measure the causal effect of some treatment variable(s) t on an outcome variable y, controlling for a set of features x.

ecosystem-graphs