Aquarium vs Gluon
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A new open source deep learning interface which allows developers to more easily and quickly build machine learning models, without compromising performance. Gluon provides a clear, concise API for defining machine learning models using a collection of pre-built, optimized neural network components. | Machine learning models are only as good as the datasets they're trained on. It helps ML teams make better models by improving their dataset quality. |
Simple, Easy-to-Understand Code: Gluon offers a full set of plug-and-play neural network building blocks, including predefined layers, optimizers, and initializers.;Flexible, Imperative Structure: Gluon does not require the neural network model to be rigidly defined, but rather brings the training algorithm and model closer together to provide flexibility in the development process.;Dynamic Graphs: Gluon enables developers to define neural network models that are dynamic, meaning they can be built on the fly, with any structure, and using any of Python’s native control flow.;High Performance: Gluon provides all of the above benefits without impacting the training speed that the underlying engine provides. | Upload your dataset to get a health check of its quality, quantity, and diversity. Zoom in and out of your dataset. Uncover distribution biases before you train. Find and fix labeling errors quickly;
Upload model inferences against your labeled datasets and deep dive into its performance. Find where your model is performing well and badly so you can take the best actions to improve it;
With knowledge of your dataset diversity and model performance, it automatically samples the best data to sample to label and retrain on. Your model performance just gets better |
Statistics | |
GitHub Stars 2.3K | GitHub Stars - |
GitHub Forks 219 | GitHub Forks - |
Stacks 29 | Stacks 9 |
Followers 80 | Followers 11 |
Votes 3 | Votes 0 |
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What are some alternatives to Gluon, Aquarium?
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scikit-learn
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H2O
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