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 # CNTK
 Microsoft Computational Network Toolkit (CNTK) is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.
 
+Go to the [CNTK Wiki](https://github.com/Microsoft/CNTK/wiki) for all information on the kit, including how to set up CNTK on your machine, how to use CNTK, examples, etc.
+
 See [LICENSE.md](./LICENSE.md) in the root of this repository for the full license information.
 
-Go to the [CNTK Wiki](https://github.com/Microsoft/CNTK/wiki) for all information on the kit, including how to set up CNTK on your machine, how to use CNTK, etc.
+## Project Description
+
+CNTK, the Computational Network Toolkit by Microsoft Research, 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.
+
+Blog:  [Microsoft Computational Network Toolkit offers most efficient distributed deep learning computational performance](http://blogs.technet.com/b/inside_microsoft_research/archive/2015/12/07/microsoft-computational-network-toolkit-offers-most-efficient-distributed-deep-learning-computational-performance.aspx)
+
+Tutorial: [Microsoft Computational Network Toolkit (CNTK) @ NIPS 2015 Workshops](http://research.microsoft.com/en-us/um/people/dongyu/CNTK-Tutorial-NIPS2015.pdf)
+
+## Performance
+
+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.
+
+![Performance chart](http://download-codeplex.sec.s-msft.com/Download?ProjectName=cntk&DownloadId=1526166)
+
+## Citation
+
+If you used this toolkit or part of it to do your research, please cite the work as:
+
+Amit Agarwal, Eldar Akchurin, Chris Basoglu, Guoguo Chen, Scott Cyphers, Jasha Droppo, Adam Eversole, Brian Guenter, Mark Hillebrand, 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, Marko Padmilac, Hari Parthasarathi, Baolin Peng, 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"](http://research.microsoft.com/apps/pubs/?id=226641), Microsoft Technical Report MSR-TR-2014-112, 2014.
+
+## Disclaimer 
+
+CNTK is a research code and ongoing project. There will be bugs in places.