josephmisiti/awesome-machine-learning
1
1
Fork 0
mirror of https://github.com/josephmisiti/awesome-machine-learning.git synced 2024-12-24 18:22:57 +03:00
Code Issues Packages Projects Releases Wiki Activity

Python general purpose ML library added

Browse Source
This commit is contained in:
zohamid 2017-10-16 08:53:44 +05:30
parent cd21db4488
commit 198377bc4f
1 changed files with 1 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

1
README.md
View File

@ -767,6 +767,7 @@ on MNIST digits[DEEP LEARNING]
<a name="python-general-purpose"></a>
#### General-Purpose Machine Learning
* [CNTK](https://github.com/Microsoft/CNTK) - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit. Documentation can be found [here](https://docs.microsoft.com/cognitive-toolkit/).
* [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning for production and analytics. Lets you focus on the fun parts of ML, while outputting production-ready code, and detailed analytics of your dataset and results. Includes support for NLP, XGBoost, LightGBM, and soon, deep learning.
* [machine learning](https://github.com/jeff1evesque/machine-learning) - automated build consisting of a [web-interface](https://github.com/jeff1evesque/machine-learning#web-interface), and set of [programmatic-interface](https://github.com/jeff1evesque/machine-learning#programmatic-interface) API, for support vector machines. Corresponding dataset(s) are stored into a SQL database, then generated model(s) used for prediction(s), are stored into a NoSQL datastore.
* [XGBoost](https://github.com/dmlc/xgboost) - Python bindings for eXtreme Gradient Boosting (Tree) Library