**The [CNTK Wiki](https://github.com/Microsoft/CNTK/wiki) has all information on CNTK including [setup](https://github.com/Microsoft/CNTK/wiki/Setup-CNTK-on-your-machine), [examples](https://github.com/Microsoft/CNTK/wiki/Examples), etc.**
***2017-01-10.*** CNTK for Windows supports Visual 2015
If you pull or merge the master branch, CNTK will now require Visual Studio 2015 to build on Windows. There are two ways to move your development environment to Visual Studio 2015:
[Migrate VS2013 to VS2015](https://github.com/Microsoft/CNTK/wiki/Setup-Migrate-VS13-to-VS15):
This gives you a fine grained control over where components are installed
* Python API behaviour is changed to be more strict.
* New Examples and Tutorials ([Artistic Style Transfer](https://github.com/Microsoft/CNTK/blob/v2.0.beta7.0/Tutorials/CNTK_205_Artistic_Style_Transfer.ipynb)
and [GoogLeNet (Inception V3)](https://github.com/Microsoft/CNTK/tree/v2.0.beta7.0/Examples/Image/Classification/GoogLeNet)
).
* New version of CNTK Evaluation library NuGet Package.
* New Examples and Tutorials: [Video action recognition](https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Examples/Video/GettingStarted), [Finance Timeseries with Pandas/Numpy](https://github.com/Microsoft/CNTK/blob/v2.0.beta6.0/Tutorials/CNTK_104_Finance_Timeseries_Basic_with_Pandas_Numpy.ipynb), [Neural Character Language Models](https://github.com/Microsoft/CNTK/tree/v2.0.beta6.0/Examples/Text/CharacterLM/README.md)
* The Windows binary packages are now created using the NVIDIA CUDA 8 toolkit, see the [release notes](https://github.com/Microsoft/CNTK/wiki/CNTK_2_0_beta_5_Release_Notes) for details. The CNTK-Linux binary packages are still built with CUDA 7.5. The Linux support for Cuda8 will follow shortly!
* Performance enhancements for evaluation of bitmap images through the new `EvaluateRgbImage` function in the [managed Eval API](https://github.com/Microsoft/CNTK/wiki/Managed-EvalDLL-API).
* We continue to improve documentation and tutorials on an ongoing basis, in this release we added a [Sequence-to-Sequence tutorial](https://github.com/Microsoft/CNTK/blob/v2.0.beta5.0/Tutorials/CNTK_204_Sequence_To_Sequence.ipynb).
The Microsoft Cognitive Toolkit (https://www.microsoft.com/en-us/research/product/cognitive-toolkit/), is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers. CNTK has been available under an open-source license since April 2015. It is our hope that the community will take advantage of CNTK to share ideas more quickly through the exchange of open source working code.
Wiki: Go to the [CNTK Wiki](https://github.com/Microsoft/CNTK/wiki) for all information on CNTK including [setup](https://github.com/Microsoft/CNTK/wiki/Setup-CNTK-on-your-machine ), [examples](https://github.com/Microsoft/CNTK/wiki/Examples ), etc.
The figure below compares processing speed (frames processed per second) of CNTK to that of four other well-known toolkits. The configuration uses a fully connected 4-layer neural network (see our benchmark [scripts](https://github.com/Alexey-Kamenev/Benchmarks)) and an effective mini batch size (8192). All results were obtained on the same hardware with the respective latest public software versions as of Dec 3, 2015.
Amit Agarwal, Eldar Akchurin, Chris Basoglu, Guoguo Chen, Scott Cyphers, Jasha Droppo, Adam Eversole, Brian Guenter, Mark Hillebrand, T. Ryan Hoens, Xuedong Huang, Zhiheng Huang, Vladimir Ivanov, Alexey Kamenev, Philipp Kranen, Oleksii Kuchaiev, Wolfgang Manousek, Avner May, Bhaskar Mitra, Olivier Nano, Gaizka Navarro, Alexey Orlov, Hari Parthasarathi, Baolin Peng, Marko Radmilac, Alexey Reznichenko, Frank Seide, Michael L. Seltzer, Malcolm Slaney, Andreas Stolcke, Huaming Wang, Yongqiang Wang, Kaisheng Yao, Dong Yu, Yu Zhang, Geoffrey Zweig (in alphabetical order), ["An Introduction to Computational Networks and the Computational Network Toolkit"](https://research.microsoft.com/apps/pubs/?id=226641), Microsoft Technical Report MSR-TR-2014-112, 2014.
This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.
