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EthicalML / awesome-production-machine-learning

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A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

machine-learning data-mining awesome deep-learning awesome-list interpretability privacy-preserving production-machine-learning mlops privacy-preserving-machine-learning explainability responsible-ai machine-learning-operations ml-ops ml-operations privacy-preserving-ml large-scale-ml production-ml large-scale-machine-learning
  • Updated Aug 23, 2021

stellargraph / stellargraph

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Consolidate example graphs in unit tests

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adocherty
adocherty commented Nov 27, 2019

Description

Currently our unit tests are disorganized and each test creates example StellarGraph graphs in different or similar ways with no sharing of this code.

This issue is to improve the unit tests by making functions to create example graphs available to all unit tests by, for example, making them pytest fixtures at the top level of the tests (see https://docs.pytest.org/en/latest/

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good first issue sg-library

chaoyanghe / Awesome-Federated-Learning

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Federated Learning Library: https://fedml.ai

machine-learning privacy computer-vision semi-supervised-learning transfer-learning wireless-communication distributed-optimization interpretability neural-architecture-search federated-learning continual-learning vertical-federated-learning non-iid decentralized-federated-learning hierarchical-federated-learning adversarial-attack-and-defense communication-efficiency straggler-problem computation-efficiency incentive-mechanism
  • Updated Jul 20, 2021

shubhomoydas / ad_examples

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A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

streaming timeseries time-series lstm generative-adversarial-network gan rnn autoencoder ensemble-learning trees active-learning concept-drift graph-convolutional-networks interpretability anomaly-detection adversarial-attacks explaination anogan unsuperivsed nettack
  • Updated Jul 2, 2021
  • Python

jphall663 / interpretable_machine_learning_with_python

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Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

python data-science machine-learning data-mining h2o gradient-boosting-machine transparency decision-tree fairness lime accountability interpretability interpretable-ai interpretable-ml xai fatml interpretable interpretable-machine-learning iml machine-learning-interpretability
  • Updated Feb 10, 2021
  • Jupyter Notebook

ScalaConsultants / Aspect-Based-Sentiment-Analysis

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Extract Weights and scores from result

AymenBenk
AymenBenk commented Jun 29, 2021

`import numpy as np
import aspect_based_sentiment_analysis as absa
from aspect_based_sentiment_analysis import alignment
from aspect_based_sentiment_analysis import Example

text = "I love mascara"
aspects = ['mascara']

recognizer = absa.aux_models.BasicPatternRecognizer()
nlp = absa.load(pattern_recognizer=recognizer)
task = nlp(text=text, aspects=aspects)
slack = task.examples

pri

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good first issue

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