josephmisiti/awesome-machine-learning
1
1
Fork 0
mirror of https://github.com/josephmisiti/awesome-machine-learning.git synced 2024-12-25 02:33:27 +03:00
Code Issues Packages Projects Releases Wiki Activity

Adds auto_ml and machineJS- automated machine learning for JavaScript, Python, analytics, and prod

Browse Source
This commit is contained in:
Preston Parry 2016-12-06 11:50:50 -08:00 committed by GitHub
parent 723ad966c7
commit c34b136dcf
1 changed files with 2 additions and 0 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

2
README.md
View File

@ -438,6 +438,7 @@ For a list of free-to-attend meetups and local events, go [here](https://github.
* [LDA.js](https://github.com/primaryobjects/lda) - LDA topic modeling for node.js * [LDA.js](https://github.com/primaryobjects/lda) - LDA topic modeling for node.js
* [Learning.js](https://github.com/yandongliu/learningjs) - Javascript implementation of logistic regression/c4.5 decision tree * [Learning.js](https://github.com/yandongliu/learningjs) - Javascript implementation of logistic regression/c4.5 decision tree
* [Machine Learning](http://joonku.com/project/machine_learning) - Machine learning library for Node.js * [Machine Learning](http://joonku.com/project/machine_learning) - Machine learning library for Node.js
* [machineJS](https://github.com/ClimbsRocks/machineJS) - Automated machine learning, data formatting, ensembling, and hyperparameter optimization for competitions and exploration- just give it a .csv file!
* [mil-tokyo](https://github.com/mil-tokyo) - List of several machine learning libraries * [mil-tokyo](https://github.com/mil-tokyo) - List of several machine learning libraries
* [Node-SVM](https://github.com/nicolaspanel/node-svm) - Support Vector Machine for nodejs * [Node-SVM](https://github.com/nicolaspanel/node-svm) - Support Vector Machine for nodejs
* [Brain](https://github.com/harthur/brain) - Neural networks in JavaScript **[Deprecated]** * [Brain](https://github.com/harthur/brain) - Neural networks in JavaScript **[Deprecated]**
@ -760,6 +761,7 @@ on MNIST digits[DEEP LEARNING]
<a name="python-general-purpose" /> <a name="python-general-purpose" />
#### General-Purpose Machine Learning #### General-Purpose Machine Learning
* [auto_ml](https://github.com/ClimbsRocks/auto_ml) - Automated machine learning pipelines for analytics and production. Handles some standard feature engineering, feature selection, model selection, model tuning, ensembling, and advanced scoring, in addition to logging output for analysts trying to understand their datasets.
* [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. * [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 * [XGBoost](https://github.com/dmlc/xgboost) - Python bindings for eXtreme Gradient Boosting (Tree) Library
* [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python * [Bayesian Methods for Hackers](https://github.com/CamDavidsonPilon/Probabilistic-Programming-and-Bayesian-Methods-for-Hackers) - Book/iPython notebooks on Probabilistic Programming in Python