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 2024-09-17 19:29:18 +03:00
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 2024-08-29 21:13:51 +03:00
Hummingbird compiles trained ML models into tensor computation for faster inference.
Обновлено 2024-08-23 12:06:25 +03:00
Variational inference for hierarchical dynamical systems
Обновлено 2024-07-25 14:00:08 +03:00
Schema decoration for inference code
Обновлено 2024-05-17 20:49:11 +03:00
Enables inference and deployment of InnerEye-DeepLearning (https://github.com/microsoft/InnerEye-deeplearning) models as an async REST API on Azure
Обновлено 2024-03-21 12:48:29 +03:00
Self-Supervised Document-to-Document Similarity Ranking via Contextualized Language Models and Hierarchical Inference
Обновлено 2022-11-28 22:08:28 +03:00
Natural Language Processing Best Practices & Examples
machine-learning
deep-learning
nlp
natural-language-processing
best-practices
text-classification
natural-language-understanding
nlu
azure-ml
text
mlflow
pretrained-models
nli
natural-language
natural-language-inference
sota
transfomer
Обновлено 2022-08-29 16:59:54 +03:00
Knowledge Distillation as Semiparametric Inference
Обновлено 2021-05-17 23:15:27 +03:00
Demonstration of Jackknife Variational Inference for Variational Autoencoders, related to ICLR 2018 paper.
Обновлено 2018-02-21 13:36:23 +03:00