Revision 95d88730b2ad9c20845ceb0af8e1244920b3a38c authored by Manik Jindal on 06 December 2017, 05:15:50 UTC, committed by Manik Jindal on 06 December 2017, 05:15:50 UTC
commit b906b2e94748f0303f4e4f41e860d882ab434a57
Author: Manik Jindal <manikj@microsoft.com>
Date:   Tue Dec 5 18:35:02 2017 -0800

    Add whl and NuGet package links in README.md

commit 5f4ff237c5ee3a449577ec29cbf93d64273c562a
Author: Manik Jindal <manikj@microsoft.com>
Date:   Tue Dec 5 09:48:37 2017 -0800

    Bump version 2.3 to 2.3.1

commit 1d0f37072524ad1a6de36bedf43359e0596c7382
Author: Manik Jindal <manikj@microsoft.com>
Date:   Tue Dec 5 00:26:26 2017 -0800

    Add release notes

commit 78b941b3e4e8c345bd6e8e45b6180ff624756d27
Author: Manik Jindal <manikj@microsoft.com>
Date:   Tue Dec 5 00:19:23 2017 -0800

    Bump version from 2.3 to 2.3.1
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README.md
# Tutorials

In this folder you find several tutorials both for the CNTK Python API, the Python Functional API, and for BrainScript. 

## Python

The Python Jupyter notebooks in this folder cover a range of different application including 
image classification, language understanding, reinforcement learning and others. 
Additionally, the folder NumpyInterop contains a simple example of how to use 
numpy arrays as input for CNTK training and evaluation.

### Functional API (still Python)

The FunctionalAPI folder is the staging area for Tutorials written using the Python Functional API. 
The plan is to have each Tutorial in this folder translated to a more succinct style in the FunctionalAPI folder.
All FunctionalAPI Tutorials are fully tested, same as the Tutorials here.


## BrainScript

There are four detailed tutorials on how to use CNTK with BrainScript. 
A step-by-step walk through for each of these is provided in the [documentation](https://docs.microsoft.com/en-us/cognitive-toolkit/Tutorials).

* Hello World - Logistic Regression ([Details](https://docs.microsoft.com/en-us/cognitive-toolkit/Tutorials))
* Image Hands On ([Details](https://docs.microsoft.com/en-us/cognitive-toolkit/Hands-On-Labs-Image-Recognition)) 
* SLU (Language Understanding) Hands On ([Details](https://docs.microsoft.com/en-us/cognitive-toolkit/Hands-On-Labs-Language-Understanding))
* Object detection using Fast R-CNN ([Code](https://github.com/Microsoft/CNTK/tree/master/Examples/Image/Detection/FastRCNN), [Details](https://docs.microsoft.com/en-us/cognitive-toolkit/Object-Detection-using-Fast-R-CNN))
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