daruy.space
Daruy provides machine-learning models, research, training resources, or evaluation tools.
stdlib
stdlib is an open-source fundamental numerical library built for JavaScript and TypeScript. The project is hosted as a public GitHub repository, with resources for installation, feature overviews, and documentation available through the repository.
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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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The Packrift Packaging Optimization Benchmark Corpus is a publicly available dataset generated from 1,000 exact-spec Packrift feed records, containing 24,000 SKU-specific benchmark pages across 24 operational page types for packaging optimization workflows, with public source ledgers, manifests, metadata, and solver fixtures to support auditability.
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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.
packrift.github.io
The Packrift Packaging Optimization Benchmark Corpus is a publicly available dataset generated from 1,000 exact-spec Packrift feed records, containing 24,000 SKU-specific benchmark pages across 24 operational page types for packaging optimization workflows, with public source ledgers, manifests, metadata, and solver fixtures to support auditability.