Showing 1-14 of 14 projects
A PyTorch implementation of the U-Net for image semantic segmentation with high quality images.
The AI developer platform to train and fine-tune models, and manage models from experimentation to production.
A curated collection of resources for engineering managers and tech leaders.
Interpret is a framework for fitting interpretable machine learning models and explaining black-box models.
A comprehensive set of fairness metrics and algorithms to detect and mitigate bias in AI/ML models and datasets.
A C++ implementation of the Kalman filter for angle, rate, and bias estimation from sensor inputs.
A Python library that trains a large language model on data from specific time periods to reduce modern bias.
An open-source marketing mix modeling library that uses AI/ML to democratize modeling knowledge and reduce human bias.
A sampling JVM profiler without the safepoint sample bias, focused on helping developers understand their application's performance.
An explainability toolbox for developers building machine learning models with interpretability and fairness in mind.
A PyTorch implementation of a Scene Graph Generation method, with visualization and extraction capabilities.
A collection of machine learning tutorials covering various topics like anomaly detection, time series forecasting, and object detection.
Weave is a toolkit for developing AI-powered applications, built by Weights & Biases.
HyperLogLog data structure library with space-efficient sparse and LogLog-Beta implementations.
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