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Deep learning framework written in Kotlin.
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Machine Learning Toolkit for Kubernetes.
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Machine Learning Pipelines for Kubeflow.
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LabNotebook is a tool that allows you to flexibly monitor, record, save, and query all your machine learning experiments.
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Simple, concise implementations of machine learning techniques and utilities in Clojure.
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A pure Go implementation of the prediction part of GBRTs, including XGBoost and LightGBM.
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A generic approach that allows to mimic most factorization models by feature engineering.
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Lightly is a computer vision framework for self-supervised learning.
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Explaining the predictions of any machine learning classifier.
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A comprehensive toolkit to build Machine Learning applications with Rust.
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Class on machine w/ PDF, lectures, code.
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Machine Learning library for the web, Node.js and developers.
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A model-agnostic visual debugging tool for machine learning.
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A Julia package for manifold learning and nonlinear dimensionality reduction.
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Application-oriented deep reinforcement learning framework addressing real-world decision problems.
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A production-ready library for multicalibration, fairness, and bias correction in machine learning models.
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MegEngine is a fast, scalable and easy-to-use deep learning framework, with auto-differentiation.
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A platform for deploying and serving machine learning models.
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Flexible Deep Learning Framework in Julia.
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An open source robotics benchmark for meta- and multi-task reinforcement learning.
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Neural networks (boltzmann machines, feed-forward and recurrent nets), Gaussian Processes.
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A distributed machine learning framework Apache Spark.
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MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
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A minimal, educational, Pythonic implementation of autograd (~100 loc).