mindsdb - Machine Learning in one line of code

•    Python

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MindsDB's is an Explainable AutoML framework for developers. MindsDB is an automated machine learning platform that allows anyone to gain powerful insights from their data. With MindsDB, users can get fast, accurate, and interpretable answers to any of their data questions within minutes.

ml pytorch xai xai-library automl ludwig tensorflow explainable-ai explainable-ml artificial-intelligence machine-learning

AIX360 - Interpretability and explainability of data and machine learning models

•    Python

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The AI Explainability 360 toolkit is an open-source library that supports interpretability and explainability of datasets and machine learning models. The AI Explainability 360 Python package includes a comprehensive set of algorithms that cover different dimensions of explanations along with proxy explainability metrics. The AI Explainability 360 interactive experience provides a gentle introduction to the concepts and capabilities by walking through an example use case for different consumer personas. The tutorials and example notebooks offer a deeper, data scientist-oriented introduction. The complete API is also available.

explainable-ai explainable-ml trusted-ai trusted-ml machine-learning deep-learning codait artificial-intelligence explainabil xai ibm-research ibm-research-ai

awesome-machine-learning-interpretability - A curated list of awesome machine learning interpretability resources

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A curated, but probably biased and incomplete, list of awesome machine learning interpretability resources. If you want to contribute to this list (and please do!) read over the contribution guidelines, send a pull request, or contact me @jpatrickhall.

fairness xai interpretability iml fatml accountability transparency machine-learning data-science data-mining r awesome awesome-list machine-learning-interpretability interpretable-machine-learning interpretable-ml interpretable-ai interpretable-deep-learning explainable-ml

tensorwatch - Debugging, monitoring and visualization for Deep Learning and Reinforcement Learning

•    Jupyter

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TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning. It fully leverages Jupyter Notebook to show real time visualizations and offers unique capabilities to query the live training process without having to sprinkle logging statements all over. You can also use TensorWatch to build your own UIs and dashboards. In addition, TensorWatch leverages several excellent libraries for visualizing model graph, review model statistics, explain prediction and so on. TensorWatch is under heavy development with a goal of providing a research platform for debugging machine learning in one easy to use, extensible and hackable package.

ai deep-learning deeplearning machine-learning machinelearning machinelearning-python reinforcement-learning debugging debugging-tool debugger-visualizer debug monitoring explainable-ai explainable-ml saliency salient-object-detection model-visualization

diabetes_use_case - Sample use case for Xavier AI in Healthcare conference: https://www

•    Jupyter

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Recent advances enable practitioners to break open machine learning’s “black box”. From machine learning algorithms guiding analytical tests in drug manufacture, to predictive models recommending courses of treatment, to sophisticated software that can read images better than doctors, machine learning has promised a new world of healthcare where algorithms can assist, or even outperform, professionals in consistency and accuracy, saving money and avoiding potentially life-threatening mistakes. But what if your doctor told you that you were sick but could not tell you why? Imagine a hospital that hospitalized and discharged patients but was unable to provide specific justification for these decisions. For decades, this was a roadblock for the adoption of machine learning algorithms in healthcare: they could make data-driven decisions that helped practitioners, payers, and patients, but they couldn’t tell users why those decisions were made.

xgboost healthcare interpretability xai iml transparency machine-learning data-science data-mining machine-learning-interpretability interpretable-ml interpretable-machine-learning explainable-ml