daruy.space
Daruy provides machine-learning models, research, training resources, or evaluation tools.
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Commit provides machine-learning models, research, training resources, or evaluation tools.
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Daruy provides machine-learning models, research, training resources, or evaluation tools.
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0Tune is a Python library for experiment execution and hyperparameter tuning at any scale. It supports machine learning frameworks including PyTorch, XGBoost, and TensorFlow and Keras, and runs algorithms such as Population Based Training (PBT) and HyperBand/ASHA. It also integrates with hyperparameter optimization tools including Ax, BayesOpt, BOHB, Nevergrad, and Optuna.
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0Hong Jing (Jingles) is a data scientist and Alibaba PhD student who writes about machine learning and neuroscience. The site collects blog articles on topics such as reinforcement learning agents, learned optimizers, convolutional layers, transformer models and decoding brain signals.
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Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. It includes built-in datasets, prediction algorithms such as SVD and NMF, similarity measures, and cross-validation tools. The documentation states that Surprise does not support implicit ratings or content-based information.
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0Tribuo is a machine learning library written in Java, developed by Oracle Labs' Machine Learning Research Group and released under the Apache 2.0 license. It supports classification, regression, clustering, anomaly detection and multi-label classification, and provides interfaces to libraries such as XGBoost and TensorFlow along with ONNX model exchange support.
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0DagsHub is an AI platform for managing the machine learning lifecycle, covering dataset curation and annotation, experiment tracking, and model management. It supports unstructured and multimodal data such as text, images, audio, video, and documents. The platform is built on open source tools and formats. DagsHub states it does not currently provide compute resources and relies on the user's own infrastructure.
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docs.ray.io
Tune is a Python library for experiment execution and hyperparameter tuning at any scale. It supports machine learning frameworks including PyTorch, XGBoost, and TensorFlow and Keras, and runs algorithms such as Population Based Training (PBT) and HyperBand/ASHA. It also integrates with hyperparameter optimization tools including Ax, BayesOpt, BOHB, Nevergrad, and Optuna.
jinglescode.github.io
Hong Jing (Jingles) is a data scientist and Alibaba PhD student who writes about machine learning and neuroscience. The site collects blog articles on topics such as reinforcement learning agents, learned optimizers, convolutional layers, transformer models and decoding brain signals.
surpriselib.com
Surprise is a Python scikit for building and analyzing recommender systems that deal with explicit rating data. It includes built-in datasets, prediction algorithms such as SVD and NMF, similarity measures, and cross-validation tools. The documentation states that Surprise does not support implicit ratings or content-based information.
tribuo.org
Tribuo is a machine learning library written in Java, developed by Oracle Labs' Machine Learning Research Group and released under the Apache 2.0 license. It supports classification, regression, clustering, anomaly detection and multi-label classification, and provides interfaces to libraries such as XGBoost and TensorFlow along with ONNX model exchange support.
dagshub.com
DagsHub is an AI platform for managing the machine learning lifecycle, covering dataset curation and annotation, experiment tracking, and model management. It supports unstructured and multimodal data such as text, images, audio, video, and documents. The platform is built on open source tools and formats. DagsHub states it does not currently provide compute resources and relies on the user's own infrastructure.