diff --git a/README.md b/README.md index 06a4036..3a5e903 100644 --- a/README.md +++ b/README.md @@ -30,13 +30,16 @@ For a list of free machine learning books available for download, go [here](http * [shark](http://image.diku.dk/shark/sphinx_pages/build/html/index.html) * [Vowpal Wabbit (VW)](https://github.com/JohnLangford/vowpal_wabbit/wiki) - A fast out-of-core learning system. * [sofia-ml](https://code.google.com/p/sofia-ml/) - Suite of fast incremental algorithms. -* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox +* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox * [Caffe](http://caffe.berkeleyvision.org) - A deep learning framework developed with cleanliness, readability, and speed in mind. [DEEP LEARNING] * [CUDA](https://code.google.com/p/cuda-convnet/) - This is a fast C++/CUDA implementation of convolutional [DEEP LEARNING] #### Natural Language Processing * [MIT Information Extraction Toolkit](https://github.com/mit-nlp/MITIE) - C, C++, and Python tools for named entity recognition and relation extraction +#### Sequence Analysis +* [ToPS](https://github.com/ayoshiaki/tops) - This is an objected-oriented framework that facilitates the integration of probabilistic models for sequences over a user defined alphabet. + ## Clojure #### Natural Language Processing @@ -104,7 +107,7 @@ For a list of free machine learning books available for download, go [here](http #### General-Purpose Machine Learning -* [JSAT](https://code.google.com/p/java-statistical-analysis-tool/) - Numerous Machine Learning algoirhtms for classification, regresion, and clustering. +* [JSAT](https://code.google.com/p/java-statistical-analysis-tool/) - Numerous Machine Learning algoirhtms for classification, regresion, and clustering. * [MLlib in Apache Spark](http://spark.apache.org/docs/latest/mllib-guide.html) - Distributed machine learning library in Spark * [Mahout](https://github.com/apache/mahout) - Distributed machine learning * [Stanford Classifier](http://nlp.stanford.edu/software/classifier.shtml) - A classifier is a machine learning tool that will take data items and place them into one of k classes. @@ -224,7 +227,7 @@ For a list of free machine learning books available for download, go [here](http * [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 * [optim](https://github.com/torch/optim) - An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more. - * [unsup](https://github.com/koraykv/unsup) - A package for unsupervised learning in Torch. Provides modules that are compatible with nn (LinearPsd, ConvPsd, AutoEncoder, ...), and self-contained algorithms (k-means, PCA). + * [unsup](https://github.com/koraykv/unsup) - A package for unsupervised learning in Torch. Provides modules that are compatible with nn (LinearPsd, ConvPsd, AutoEncoder, ...), and self-contained algorithms (k-means, PCA). * [manifold](https://github.com/clementfarabet/manifold) - A package to manipulate manifolds * [svm](https://github.com/koraykv/torch-svm) - Torch-SVM library * [lbfgs](https://github.com/clementfarabet/lbfgs) - FFI Wrapper for liblbfgs @@ -237,10 +240,10 @@ For a list of free machine learning books available for download, go [here](http * [cunn](https://github.com/torch/cunn) - Torch CUDA Neural Network Implementation * [imgraph](https://github.com/clementfarabet/lua---imgraph) - An image/graph library for Torch. This package provides routines to construct graphs on images, segment them, build trees out of them, and convert them back to images. * [videograph](https://github.com/clementfarabet/videograph) - A video/graph library for Torch. This package provides routines to construct graphs on videos, segment them, build trees out of them, and convert them back to videos. - * [saliency](https://github.com/marcoscoffier/torch-saliency) - code and tools around integral images. A library for finding interest points based on fast integral histograms. + * [saliency](https://github.com/marcoscoffier/torch-saliency) - code and tools around integral images. A library for finding interest points based on fast integral histograms. * [stitch](https://github.com/marcoscoffier/lua---stitch) - allows us to use hugin to stitch images and apply same stitching to a video sequence * [sfm](https://github.com/marcoscoffier/lua---sfm) - A bundle adjustment/structure from motion package - * [fex](https://github.com/koraykv/fex) - A package for feature extraction in Torch. Provides SIFT and dSIFT modules. + * [fex](https://github.com/koraykv/fex) - A package for feature extraction in Torch. Provides SIFT and dSIFT modules. * [OverFeat](https://github.com/sermanet/OverFeat) - A state-of-the-art generic dense feature extractor * [Numeric Lua](http://numlua.luaforge.net/) * [Lunatic Python](http://labix.org/lunatic-python) @@ -249,7 +252,7 @@ For a list of free machine learning books available for download, go [here](http * [Lunum](http://zrake.webfactional.com/projects/lunum) #### Demos and Scripts -* [Core torch7 demos repository](https://github.com/e-lab/torch7-demos). +* [Core torch7 demos repository](https://github.com/e-lab/torch7-demos). * linear-regression, logistic-regression * face detector (training and detection as separate demos) * mst-based-segmenter @@ -372,7 +375,7 @@ on MNIST digits[DEEP LEARNING] * [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox * [Pyevolve](https://github.com/perone/Pyevolve) - Genetic algorithm framework. * [Caffe](http://caffe.berkeleyvision.org) - A deep learning framework developed with cleanliness, readability, and speed in mind. -* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox +* [Shogun](https://github.com/shogun-toolbox/shogun) - The Shogun Machine Learning Toolbox * [breze](https://github.com/breze-no-salt/breze) - Theano based library for deep and recurrent neural networks #### Data Analysis / Data Visualization