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.
machine-learning
data-science
causal-inference
causality
treatment-effects
bayesian-networks
causal-machine-learning
causal-models
do-calculus
graphical-models
python3
Обновлено 2024-11-21 01:27:34 +03:00
DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
machine-learning
deep-learning
pytorch
gpu
compression
billion-parameters
data-parallelism
inference
mixture-of-experts
model-parallelism
pipeline-parallelism
trillion-parameters
zero
Обновлено 2024-11-20 04:04:47 +03:00
HI-ML toolbox for deep learning for medical imaging and Azure integration
Обновлено 2024-11-18 20:06:15 +03:00
Best Practices on Recommendation Systems
azure
microsoft
machine-learning
python
deep-learning
kubernetes
data-science
artificial-intelligence
jupyter-notebook
tutorial
operationalization
ranking
rating
recommendation
recommendation-algorithm
recommendation-engine
recommendation-system
recommender
Обновлено 2024-11-18 12:48:34 +03:00
Platform for Machine Learning projects on Software Engineering
Обновлено 2024-11-18 06:25:17 +03:00
Hummingbird compiles trained ML models into tensor computation for faster inference.
Обновлено 2024-11-16 00:52:33 +03:00
Qlib is an AI-oriented quantitative investment platform that aims to realize the potential, empower research, and create value using AI technologies in quantitative investment, from exploring ideas to implementing productions. Qlib supports diverse machine learning modeling paradigms. including supervised learning, market dynamics modeling, and RL.
machine-learning
deep-learning
python
platform
research
finance
algorithmic-trading
auto-quant
fintech
investment
paper
quant
quant-dataset
quant-models
quantitative-finance
quantitative-trading
research-paper
stock-data
Обновлено 2024-11-13 06:41:06 +03:00
Sharing Updatable Models (SUM) on Blockchain
machine-learning
python
react
ai
ml
artificial-intelligence
node
economics
blockchain
ethereum
prediction-mar
prediction-market
smart-contracts
truffle
Обновлено 2024-10-31 16:38:58 +03:00
This package features data-science related tasks for developing new recognizers for Presidio. It is used for the evaluation of the entire system, as well as for evaluating specific PII recognizers or PII detection models.
machine-learning
deep-learning
nlp
natural-language-processing
privacy
ner
transformers
pii
named-entity-recognition
spacy
flair
Обновлено 2024-10-27 15:51:02 +03:00
Accelerate your Neural Architecture Search (NAS) through fast, reproducible and modular research.
machine-learning
deep-learning
python
pytorch
hyperparameter-optimization
automated-machine-learning
automl
model-compression
nas
neural-architecture-search
darts
petridish
Обновлено 2024-10-23 20:40:41 +03:00
Python package for graph statistics
Обновлено 2024-10-09 19:41:04 +03:00
A Repository for the public preview of Responsible AI in AML vNext
Обновлено 2024-09-13 01:24:07 +03:00
The ORBIT dataset is a collection of videos of objects in clean and cluttered scenes recorded by people who are blind/low-vision on a mobile phone. The dataset is presented with a teachable object recognition benchmark task which aims to drive few-shot learning on challenging real-world data.
microsoft
machine-learning
computer-vision
video
benchmark
dataset
classification
few-shot-learning
meta-learning
object-recognition
Обновлено 2024-08-13 03:27:45 +03:00
Support ML teams to accelerate their model deployment to production leveraging Azure
Обновлено 2024-08-05 10:21:15 +03:00
this repo provides best practice guidance, plan template, solution assessment tool etc. to help Machine Learning Studio(classic) customer adopt Azure Machine Learning.
Обновлено 2024-07-23 05:46:51 +03:00
An open source AutoML toolkit for automate machine learning lifecycle, including feature engineering, neural architecture search, model compression and hyper-parameter tuning.
machine-learning
deep-learning
data-science
python
tensorflow
mlops
pytorch
hyperparameter-optimization
hyperparameter-tuning
machine-learning-algorithms
model-compression
nas
neural-architecture-search
neural-network
automated-machine-learning
automl
bayesian-optimization
deep-neural-network
distributed
feature-engineering
Обновлено 2024-07-03 13:54:08 +03:00
Using machine learning to detect beluga whale calls in hydrophone recordings
Обновлено 2024-06-18 01:58:02 +03:00
Common utilities for ONNX converters
Обновлено 2024-06-14 03:56:41 +03:00
Federated Learning Utilities and Tools for Experimentation
machine-learning
pytorch
simulation
gloo
nccl
personalization
privacy-tools
transformers-models
distributed-learning
federated-learning
Обновлено 2024-01-11 22:20:09 +03:00
Toolkit for building machine learning models that generalize to unseen domains and are robust to privacy and other attacks.
machine-learning
artificial-intelligence
causality
domain-generalization
privacy-preserving-machine-learning
Обновлено 2023-10-03 07:31:52 +03:00
Visuomotor policies from event-based cameras through representation learning and reinforcement learning. Accompanies our paper: https://arxiv.org/abs/2103.00806
Обновлено 2023-08-15 00:50:21 +03:00
Azure MLOps (v2) solution accelerators. Enterprise ready templates to deploy your machine learning models on the Azure Platform.
azure
microsoft
machine-learning
deep-learning
devops
azureml
mlops
azuremachinelearning
mlops-environment
mlops-template
mlops-workflow
Обновлено 2023-07-28 08:11:02 +03:00
Examples of how to use or integrate DeepSpeech
Обновлено 2023-07-25 21:07:54 +03:00
Lite Self-Training
Обновлено 2023-07-25 20:31:38 +03:00
Workshop for student hackathons focused on IoT dev
Обновлено 2023-07-25 12:28:46 +03:00
Self-training with Weak Supervision (NAACL 2021)
Обновлено 2023-07-25 01:35:52 +03:00
Cookiecutter template for testing Python scikit-learn regression learners.
Обновлено 2023-06-12 22:39:27 +03:00
Azure Machine Learning と GitHub を利用した MLOps のサンプルコード
azure
microsoft
python
machine-learning
devops
azure-machine-learning
mlops
scikit-learn
responsible-ai
pytest
mypy
Обновлено 2023-06-07 05:04:08 +03:00
Ready to use scoring engines for Image, Text and Time Series
Обновлено 2023-05-31 21:48:19 +03:00
Time Series Forecasting Best Practices & Examples
machine-learning
python
deep-learning
artificial-intelligence
r
best-practices
jupyter-notebook
automl
lightgbm
hyperparameter-tuning
model-deployment
prophet
retail
tidyverse
time-series
azure-ml
demand-forecasting
dilated-cnn
forecasting
Обновлено 2023-05-01 00:54:37 +03:00