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EthicalML / awesome-production-machine-learning

Star 11.3k

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 Mar 25, 2022

jacobgil / pytorch-grad-cam

Star 4.6k

Many Class Activation Map methods implemented in Pytorch for CNNs and Vision Transformers. Examples for classification, object detection, segmentation, embedding networks and more. Including Grad-CAM, Grad-CAM++, Score-CAM, Ablation-CAM and XGrad-CAM

deep-learning grad-cam pytorch visualizations interpretability class-activation-maps interpretable-deep-learning interpretable-ai score-cam vision-transformers
  • Updated Apr 4, 2022
  • Python

stellargraph / stellargraph

Star 2.3k
Open

Consolidate example graphs in unit tests

2
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

SeldonIO / alibi

Star 1.6k
Open

Replace Boston dataset in examples

5
jklaise
jklaise commented Sep 28, 2021

The Boston dataset which we use in some examples has an ethical problem and should be replaced. Read more here: https://scikit-learn.org/stable/modules/generated/sklearn.datasets.load_boston.html#sklearn.datasets.load_boston

Impacted examples:

  • cfproto_housing.ipynb
  • ale_regression_boston.ipynb

The above link suggests some similar housing-related alternatives.

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Good first issue Type: Docs
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chaoyanghe / Awesome-Federated-Learning

Star 1.3k

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 Mar 8, 2022

shubhomoydas / ad_examples

Star 755

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 Feb 10, 2022
  • Python

jphall663 / interpretable_machine_learning_with_python

Star 592

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

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