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Merge pull request #341 from Depado/fixing_dead_link

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Fixing the Snowball Golang repository link (Bitbucket one doesn't exsist anymore)
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Joseph Misiti 2017-01-24 12:51:35 -05:00 committed by GitHub
commit 4de5b3e674
1 changed files with 2 additions and 2 deletions
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@ -276,7 +276,7 @@ For a list of free-to-attend meetups and local events, go [here](https://github.
* [go-porterstemmer](https://github.com/reiver/go-porterstemmer) - A native Go clean room implementation of the Porter Stemming algorithm.
* [paicehusk](https://github.com/Rookii/paicehusk) - Golang implementation of the Paice/Husk Stemming Algorithm.
* [snowball](https://bitbucket.org/tebeka/snowball) - Snowball Stemmer for Go.
* [snowball](https://github.com/tebeka/snowball) - Snowball Stemmer for Go.
* [go-ngram](https://github.com/Lazin/go-ngram) - In-memory n-gram index with compression.
<a name="go-general-purpose" />
@ -762,7 +762,7 @@ on MNIST digits[DEEP LEARNING]
<a name="python-general-purpose" />
#### 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.
* [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.
* [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