josephmisiti/awesome-machine-learning
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@ -521,6 +521,7 @@ Further resources:
* [TensorFlow.js](https://js.tensorflow.org/) - A WebGL accelerated, browser based JavaScript library for training and deploying ML models.
* [JSMLT](https://github.com/jsmlt/jsmlt) - Machine learning toolkit with classification and clustering for Node.js; supports visualization (see [visualml.io](https://visualml.io)).
* [xgboost-node](https://github.com/nuanio/xgboost-node) - Run XGBoost model and make predictions in Node.js.
* [Netron](https://github.com/lutzroeder/netron) - Visualizer for machine learning models.
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#### Misc
@ -988,6 +989,7 @@ be
* [Turi Create](https://github.com/apple/turicreate) - Machine learning from Apple. Turi Create simplifies the development of custom machine learning models. You don't have to be a machine learning expert to add recommendations, object detection, image classification, image similarity or activity classification to your app.
* [xLearn](https://github.com/aksnzhy/xlearn) - A high performance, easy-to-use, and scalable machine learning package, which can be used to solve large-scale machine learning problems. xLearn is especially useful for solving machine learning problems on large-scale sparse data, which is very common in Internet services such as online advertisement and recommender systems.
* [mlens](https://github.com/flennerhag/mlens) - A high performance, memory efficient, maximally parallelized ensemble learning, integrated with scikit-learn.
* [Netron](https://github.com/lutzroeder/netron) - Visualizer for machine learning models.
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#### Data Analysis / Data Visualization