Aerosolve vs Lightly
Overview
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CLI (Node.js)
Manual
Detailed Comparison
This library is meant to be used with sparse, interpretable features such as those that commonly occur in search (search keywords, filters) or pricing (number of rooms, location, price). It is not as interpretable with problems with very dense non-human interpretable features such as raw pixels or audio samples. | 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. |
A thrift based feature representation that enables pairwise ranking loss and single context multiple item representation.;A feature transform language gives the user a lot of control over the features;Human friendly debuggable models;Separate lightweight Java inference code;Scala code for training;Simple image content analysis code suitable for ordering or ranking images | Save money on your data related costs by removing redundancies;
Reduce overfitting and improve generalization by diversifying your dataset |
Statistics | |
GitHub Stars 4.8K | GitHub Stars 3.6K |
GitHub Forks 565 | GitHub Forks 313 |
Stacks 27 | Stacks 0 |
Followers 73 | Followers 1 |
Votes 0 | Votes 0 |
Integrations | |
| No integrations available | |
What are some alternatives to Aerosolve, Lightly?
Cloudinary
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imgix
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TensorFlow
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OpenCV
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scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
PyTorch
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ImageKit
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Keras
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Kubeflow
The Kubeflow project is dedicated to making Machine Learning on Kubernetes easy, portable and scalable by providing a straightforward way for spinning up best of breed OSS solutions.
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
