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@ -156,6 +156,7 @@ For a list of free machine learning books available for download, go [here](http
* [Warp-CTC](https://github.com/baidu-research/warp-ctc) - A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU. * [Warp-CTC](https://github.com/baidu-research/warp-ctc) - A fast parallel implementation of Connectionist Temporal Classification (CTC), on both CPU and GPU.
* [CNTK](https://github.com/Microsoft/CNTK) - The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. * [CNTK](https://github.com/Microsoft/CNTK) - The Computational Network Toolkit (CNTK) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.
* [DeepDetect](https://github.com/beniz/deepdetect) - A machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications. * [DeepDetect](https://github.com/beniz/deepdetect) - A machine learning API and server written in C++11. It makes state of the art machine learning easy to work with and integrate into existing applications.
* [Fido](https://github.com/FidoProject/Fido) - A highly-modular C++ machine learning library for embedded electronics and robotics.
<a name="cpp-nlp" /> <a name="cpp-nlp" />
#### Natural Language Processing #### Natural Language Processing
@ -203,7 +204,7 @@ For a list of free machine learning books available for download, go [here](http
#### General-Purpose Machine Learning #### General-Purpose Machine Learning
* [Touchstone](https://github.com/ptaoussanis/touchstone) - Clojure A/B testing library * [Touchstone](https://github.com/ptaoussanis/touchstone) - Clojure A/B testing library
* [Clojush](https://github.com/lspector/Clojush) - he Push programming language and the PushGP genetic programming system implemented in Clojure * [Clojush](https://github.com/lspector/Clojush) - The Push programming language and the PushGP genetic programming system implemented in Clojure
* [Infer](https://github.com/aria42/infer) - Inference and machine learning in clojure * [Infer](https://github.com/aria42/infer) - Inference and machine learning in clojure
* [Clj-ML](https://github.com/antoniogarrote/clj-ml) - A machine learning library for Clojure built on top of Weka and friends * [Clj-ML](https://github.com/antoniogarrote/clj-ml) - A machine learning library for Clojure built on top of Weka and friends
* [Encog](https://github.com/jimpil/enclog) - Clojure wrapper for Encog (v3) (Machine-Learning framework that specializes in neural-nets) * [Encog](https://github.com/jimpil/enclog) - Clojure wrapper for Encog (v3) (Machine-Learning framework that specializes in neural-nets)
@ -491,13 +492,16 @@ For a list of free machine learning books available for download, go [here](http
* [Torch7](http://torch.ch/) * [Torch7](http://torch.ch/)
* [cephes](http://jucor.github.io/torch-cephes) - Cephes mathematical functions library, wrapped for Torch. Provides and wraps the 180+ special mathematical functions from the Cephes mathematical library, developed by Stephen L. Moshier. It is used, among many other places, at the heart of SciPy. * [cephes](http://jucor.github.io/torch-cephes) - Cephes mathematical functions library, wrapped for Torch. Provides and wraps the 180+ special mathematical functions from the Cephes mathematical library, developed by Stephen L. Moshier. It is used, among many other places, at the heart of SciPy.
* [autograd](https://github.com/twitter/torch-autograd) - Autograd automatically differentiates native Torch code. Inspired by the original Python version.
* [graph](https://github.com/torch/graph) - Graph package for Torch * [graph](https://github.com/torch/graph) - Graph package for Torch
* [randomkit](http://jucor.github.io/torch-randomkit/) - Numpy's randomkit, wrapped for Torch * [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 * [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 * [nn](https://github.com/torch/nn) - Neural Network package for Torch
* [nngraph](https://github.com/torch/nngraph) - This package provides graphical computation for nn library in Torch7. * [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 * [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.
* [dpnn](https://github.com/Element-Research/dpnn) - Many useful features that aren't part of the main nn package.
* [dp](https://github.com/nicholas-leonard/dp) - A deep learning library designed for streamlining research and development using the Torch7 distribution. It emphasizes flexibility through the elegant use of object-oriented design patterns.
* [optim](https://github.com/torch/optim) - An optimization library for Torch. SGD, Adagrad, Conjugate-Gradient, LBFGS, RProp and more. * [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 * [manifold](https://github.com/clementfarabet/manifold) - A package to manipulate manifolds
@ -796,6 +800,7 @@ on MNIST digits[DEEP LEARNING]
* [bqplot](https://github.com/bloomberg/bqplot) - An API for plotting in Jupyter (IPython) * [bqplot](https://github.com/bloomberg/bqplot) - An API for plotting in Jupyter (IPython)
* [pastalog](https://github.com/rewonc/pastalog) - Simple, realtime visualization of neural network training performance. * [pastalog](https://github.com/rewonc/pastalog) - Simple, realtime visualization of neural network training performance.
* [caravel](https://github.com/airbnb/caravel) - A data exploration platform designed to be visual, intuitive, and interactive. * [caravel](https://github.com/airbnb/caravel) - A data exploration platform designed to be visual, intuitive, and interactive.
* [Dora](https://github.com/nathanepstein/dora) - Tools for exploratory data analysis in Python.
<a name="python-misc" /> <a name="python-misc" />
#### Misc Scripts / iPython Notebooks / Codebases #### Misc Scripts / iPython Notebooks / Codebases
@ -1095,6 +1100,7 @@ on MNIST digits[DEEP LEARNING]
includes a general matrix language and wraps some OpenCV for iOS development. includes a general matrix language and wraps some OpenCV for iOS development.
* [DeepLearningKit](http://deeplearningkit.org/) an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS. * [DeepLearningKit](http://deeplearningkit.org/) an Open Source Deep Learning Framework for Apple’s iOS, OS X and tvOS.
It currently allows using deep convolutional neural network models trained in Caffe on Apple operating systems. It currently allows using deep convolutional neural network models trained in Caffe on Apple operating systems.
* [AIToolbox](https://github.com/KevinCoble/AIToolbox) - A toolbox framework of AI modules written in Swift: Graphs/Trees, Linear Regression, Support Vector Machines, Neural Networks, PCA, KMeans, Genetic Algorithms, MDP, Mixture of Gaussians.
<a name="credits" /> <a name="credits" />
## Credits ## Credits