josephmisiti/awesome-machine-learning
1
1
Fork 0
mirror of https://github.com/josephmisiti/awesome-machine-learning.git synced 2025-01-03 15:13:22 +03:00
Code Issues Packages Projects Releases Wiki Activity

Merge remote-tracking branch 'josephmisiti/master'

Browse Source
This commit is contained in:
Vincent Botta 2016-07-10 19:42:59 +02:00
commit 91967ab5f4
2 changed files with 8 additions and 1 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
README.md
View File

@ -339,6 +339,7 @@ For a list of free machine learning books available for download, go [here](http
* [Weka](http://www.cs.waikato.ac.nz/ml/weka/) - Weka is a collection of machine learning algorithms for data mining tasks
* [LBJava](https://github.com/IllinoisCogComp/lbjava/) - Learning Based Java is a modeling language for the rapid development of software systems, offers a convenient, declarative syntax for classifier and constraint definition directly in terms of the objects in the programmer's application.
#### Speech Recognition
* [CMU Sphinx](http://cmusphinx.sourceforge.net/) - Open Source Toolkit For Speech Recognition purely based on Java speech recognition library.
@ -514,6 +515,7 @@ For a list of free machine learning books available for download, go [here](http
* [randomkit](http://jucor.github.io/torch-randomkit/) - Numpy's randomkit, wrapped for Torch
* [signal](http://soumith.ch/torch-signal/signal/) - A signal processing toolbox for Torch-7. FFT, DCT, Hilbert, cepstrums, stft
* [nn](https://github.com/torch/nn) - Neural Network package for Torch
* [torchnet](https://github.com/torchnet/torchnet) - framework for torch which provides a set of abstractions aiming at encouraging code re-use as well as encouraging modular programming
* [nngraph](https://github.com/torch/nngraph) - This package provides graphical computation for nn library in Torch7.
* [nnx](https://github.com/clementfarabet/lua---nnx) - A completely unstable and experimental package that extends Torch's builtin nn library
* [rnn](https://github.com/Element-Research/rnn) - A Recurrent Neural Network library that extends Torch's nn. RNNs, LSTMs, GRUs, BRNNs, BLSTMs, etc.
@ -720,7 +722,7 @@ on MNIST digits[DEEP LEARNING]
* [PyStanfordDependencies](https://github.com/dmcc/PyStanfordDependencies) - Python interface for converting Penn Treebank trees to Stanford Dependencies.
* [Distance](https://github.com/doukremt/distance) - Levenshtein and Hamming distance computation
* [Fuzzy Wuzzy](https://github.com/seatgeek/fuzzywuzzy) - Fuzzy String Matching in Python
* [jellyfish](https://github.com/jamesturk/jellyfishå) - a python library for doing approximate and phonetic matching of strings.
* [jellyfish](https://github.com/jamesturk/jellyfish) - a python library for doing approximate and phonetic matching of strings.
* [editdistance](https://pypi.python.org/pypi/editdistance) - fast implementation of edit distance
* [textacy](https://github.com/chartbeat-labs/textacy) - higher-level NLP built on Spacy
@ -741,6 +743,7 @@ on MNIST digits[DEEP LEARNING]
* [NuPIC](https://github.com/numenta/nupic) - Numenta Platform for Intelligent Computing.
* [Pylearn2](https://github.com/lisa-lab/pylearn2) - A Machine Learning library based on [Theano](https://github.com/Theano/Theano).
* [keras](https://github.com/fchollet/keras) - Modular neural network library based on [Theano](https://github.com/Theano/Theano).
* [Lasagne](https://github.com/Lasagne/Lasagne) - Lightweight library to build and train neural networks in Theano.
* [hebel](https://github.com/hannes-brt/hebel) - GPU-Accelerated Deep Learning Library in Python.
* [Chainer](https://github.com/pfnet/chainer) - Flexible neural network framework
* [gensim](https://github.com/piskvorky/gensim) - Topic Modelling for Humans.
@ -863,6 +866,7 @@ on MNIST digits[DEEP LEARNING]
* [Optunity examples](http://docs.optunity.net/notebooks/index.html) - Examples demonstrating how to use Optunity in synergy with machine learning libraries.
* [Dive into Machine Learning with Python Jupyter notebook and scikit-learn](https://github.com/hangtwenty/dive-into-machine-learning) - "I learned Python by hacking first, and getting serious *later.* I wanted to do this with Machine Learning. If this is your style, join me in getting a bit ahead of yourself."
* [TDB](https://github.com/ericjang/tdb) - TensorDebugger (TDB) is a visual debugger for deep learning. It features interactive, node-by-node debugging and visualization for TensorFlow.
* [Introduction to machine learning with scikit-learn](https://github.com/justmarkham/scikit-learn-videos) - IPython notebooks from Data School's video tutorials on scikit-learn.
<a name="python-neural networks"/>
@ -1121,6 +1125,7 @@ on MNIST digits[DEEP LEARNING]
* [H2O Sparkling Water](https://github.com/h2oai/sparkling-water) - H2O and Spark interoperability.
* [FlinkML in Apache Flink](https://ci.apache.org/projects/flink/flink-docs-master/apis/batch/libs/ml/index.html) - Distributed machine learning library in Flink
* [DynaML](https://github.com/mandar2812/DynaML) - Scala Library/REPL for Machine Learning Research
* [Saul](https://github.com/IllinoisCogComp/saul/) - Flexible Declarative Learning-Based Programming.
<a name="swift" />
## Swift

2
blogs.md
View File

@ -91,6 +91,8 @@ http://www.randalolson.com/blog/
http://www.johndcook.com/blog/r_language_for_programmers/
http://www.dataschool.io/
Math
----