commit
04dba04a35
|
@ -222,6 +222,7 @@ Further resources:
|
||||||
* [MLDB](https://mldb.ai) - The Machine Learning Database is a database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
|
* [MLDB](https://mldb.ai) - The Machine Learning Database is a database designed for machine learning. Send it commands over a RESTful API to store data, explore it using SQL, then train machine learning models and expose them as APIs.
|
||||||
* [mlpack](https://www.mlpack.org/) - A scalable C++ machine learning library.
|
* [mlpack](https://www.mlpack.org/) - A scalable C++ machine learning library.
|
||||||
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
|
* [MXNet](https://github.com/apache/incubator-mxnet) - Lightweight, Portable, Flexible Distributed/Mobile Deep Learning with Dynamic, Mutation-aware Dataflow Dep Scheduler; for Python, R, Julia, Go, Javascript and more.
|
||||||
|
* [N2D2](https://github.com/CEA-LIST/N2D2) - CEA-List's CAD framework for designing and simulating Deep Neural Network, and building full DNN-based applications on embedded platforms
|
||||||
* [oneDNN](https://github.com/oneapi-src/oneDNN) - An open-source cross-platform performance library for deep learning applications.
|
* [oneDNN](https://github.com/oneapi-src/oneDNN) - An open-source cross-platform performance library for deep learning applications.
|
||||||
* [ParaMonte](https://github.com/cdslaborg/paramonte) - A general-purpose library with C/C++ interface for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found [here](https://www.cdslab.org/paramonte/).
|
* [ParaMonte](https://github.com/cdslaborg/paramonte) - A general-purpose library with C/C++ interface for Bayesian data analysis and visualization via serial/parallel Monte Carlo and MCMC simulations. Documentation can be found [here](https://www.cdslab.org/paramonte/).
|
||||||
* [proNet-core](https://github.com/cnclabs/proNet-core) - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
|
* [proNet-core](https://github.com/cnclabs/proNet-core) - A general-purpose network embedding framework: pair-wise representations optimization Network Edit.
|
||||||
|
|
Loading…
Reference in New Issue