Displaying 1 to 7 from 7 results

xai - XAI - An eXplainability toolbox for machine learning

  •    Python

XAI is a Machine Learning library that is designed with AI explainability in its core. XAI contains various tools that enable for analysis and evaluation of data and models. The XAI library is maintained by The Institute for Ethical AI & ML, and it was developed based on the 8 principles for Responsible Machine Learning. You can find the documentation at https://ethicalml.github.io/xai/index.html. You can also check out our talk at Tensorflow London where the idea was first conceived - the talk also contains an insight on the definitions and principles in this library.

mindsdb - Machine Learning in one line of code

  •    Python

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.

AIX360 - Interpretability and explainability of data and machine learning models

  •    Python

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.

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

  •    Jupyter

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.




goNEAT_NS - This project provides GOLang implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization aimed to solve deceptive tasks with strong local optima

  •    Go

This repository provides implementation of Neuro-Evolution of Augmented Topologies (NEAT) with Novelty Search optimization implemented in GoLang. The Neuro-Evolution (NE) is an artificial evolution of Neural Networks (NN) using genetic algorithms in order to find optimal NN parameters and topology. Neuro-Evolution of NN may assume search for optimal weights of connections between NN nodes as well as search for optimal topology of resulting NN. The NEAT method implemented in this work do search for both: optimal connections weights and topology for given task (number of NN nodes per layer and their interconnections).

ml-fairness-framework - FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

  •    Jupyter

Animated Investment Management Research at Sov.ai — Sponsoring open source AI, Machine learning, and Data Science initiatives. This is a holistic approach to implement fair outputs at the individual and group level. Some of the methods developed or used includes quantitative monotonic measures, residual explanations, benchmark competition, adverserial attacks, disparate error analysis, model agnostic pre-and post-processing, reasoning codes, counterfactuals, contrastive explanations, and prototypical examples.






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