Gluon vs Lightly
Overview
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Detailed Comparison
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. | Data labeling is time-consuming and can be very expensive. Lightly tells companies which data to label to have the biggest impact on model accuracy while saving time and costs. |
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. | Save money on your data related costs by removing redundancies;
Reduce overfitting and improve generalization by diversifying your dataset |
Statistics | |
GitHub Stars 2.3K | GitHub Stars 3.6K |
GitHub Forks 219 | GitHub Forks 313 |
Stacks 29 | Stacks 0 |
Followers 80 | Followers 1 |
Votes 3 | Votes 0 |
Pros & Cons | |
Pros
| No community feedback yet |
Integrations | |
| No integrations available | |
What are some alternatives to Gluon, Lightly?
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TensorFlow
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OpenCV
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scikit-learn
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PyTorch
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Keras
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TensorFlow.js
Use flexible and intuitive APIs to build and train models from scratch using the low-level JavaScript linear algebra library or the high-level layers API
