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Added reference to Intel(R) DAAL

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Ruslan Israfilov 2016-08-11 22:31:59 +03:00
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@ -170,6 +170,7 @@ For a list of free machine learning books available for download, go [here](http
* [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.
* [DSSTNE](https://github.com/amznlabs/amazon-dsstne) - A software library created by Amazon for training and deploying deep neural networks using GPUs which emphasizes speed and scale over experimental flexibility.
* [Intel(R) DAAL](https://github.com/01org/daal) - A high performance software library developed by Intel and optimized for Intel's architectures. Library provides algorithmic building blocks for all stages of data analytics and allows to process data in batch, online and distributed modes.
<a name="cpp-nlp" />
#### Natural Language Processing
@ -337,7 +338,7 @@ For a list of free machine learning books available for download, go [here](http
* [SystemML](https://github.com/apache/incubator-systemml) - flexible, scalable machine learning (ML) language.
* [WalnutiQ](https://github.com/WalnutiQ/WalnutiQ) - object oriented model of the human brain
* [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.
* [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
@ -1129,7 +1130,7 @@ 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.
* [Saul](https://github.com/IllinoisCogComp/saul/) - Flexible Declarative Learning-Based Programming.
<a name="swift" />
## Swift
@ -1144,7 +1145,7 @@ on MNIST digits[DEEP LEARNING]
* [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.
* [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.
* [MLKit](https://github.com/Somnibyte/MLKit) - A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.
* [MLKit](https://github.com/Somnibyte/MLKit) - A simple Machine Learning Framework written in Swift. Currently features Simple Linear Regression, Polynomial Regression, and Ridge Regression.
<a name="tensor" />
## TensorFlow