Merge pull request #456 from zeyiwen/master

add ThunderSVM
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Joseph Misiti 2017-12-04 16:16:34 -05:00 committed by GitHub
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@ -674,6 +674,7 @@ on MNIST digits[DEEP LEARNING].
* [t-Distributed Stochastic Neighbor Embedding](http://homepage.tudelft.nl/19j49/t-SNE.html) - t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets. * [t-Distributed Stochastic Neighbor Embedding](http://homepage.tudelft.nl/19j49/t-SNE.html) - t-Distributed Stochastic Neighbor Embedding (t-SNE) is a (prize-winning) technique for dimensionality reduction that is particularly well suited for the visualization of high-dimensional datasets.
* [Spider](http://people.kyb.tuebingen.mpg.de/spider/) - The spider is intended to be a complete object orientated environment for machine learning in Matlab. * [Spider](http://people.kyb.tuebingen.mpg.de/spider/) - The spider is intended to be a complete object orientated environment for machine learning in Matlab.
* [LibSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab) - A Library for Support Vector Machines. * [LibSVM](http://www.csie.ntu.edu.tw/~cjlin/libsvm/#matlab) - A Library for Support Vector Machines.
* [ThunderSVM](https://github.com/zeyiwen/thundersvm) - An Open-Source SVM Library on GPUs and CPUs
* [LibLinear](http://www.csie.ntu.edu.tw/~cjlin/liblinear/#download) - A Library for Large Linear Classification. * [LibLinear](http://www.csie.ntu.edu.tw/~cjlin/liblinear/#download) - A Library for Large Linear Classification.
* [Machine Learning Module](https://github.com/josephmisiti/machine-learning-module) - Class on machine w/ PDF, lectures, code * [Machine Learning Module](https://github.com/josephmisiti/machine-learning-module) - Class on machine w/ PDF, lectures, code
* [Caffe](http://caffe.berkeleyvision.org) - A deep learning framework developed with cleanliness, readability, and speed in mind. * [Caffe](http://caffe.berkeleyvision.org) - A deep learning framework developed with cleanliness, readability, and speed in mind.