Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
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Application-oriented deep reinforcement learning framework addressing real-world decision problems.
A production-ready library for multicalibration, fairness, and bias correction in machine learning models.
MegEngine is a fast, scalable and easy-to-use deep learning framework, with auto-differentiation.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
A platform for deploying and serving machine learning models.
Flexible Deep Learning Framework in Julia.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
An open source robotics benchmark for meta- and multi-task reinforcement learning.
Neural networks (boltzmann machines, feed-forward and recurrent nets), Gaussian Processes.
A distributed machine learning framework Apache Spark.
MindSpore is a new open source deep learning training/inference framework that could be used for mobile, edge and cloud scenarios.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
A minimal, educational, Pythonic implementation of autograd (~100 loc).
An asynchronous engine for continuous & autonomous machine learning, built for real-time usage.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
A Repository Containing Classification, Clustering, Regression, Recommender Notebooks with illustration to make them.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
All-in-one web-based IDE specialized for machine learning and data science.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
Machine learning and numerical analysis tools for Node.js and the Browser!
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.
500+ ML/AI interview Q&A with runnable code — covers ML fundamentals, deep learning, NLP, PyTorch, scikit-learn pipelines, and system design.
Open-source project that provides machine-learning models, research, training resources, or evaluation tools.