Update README.md

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* MXNet
* training - generated is Python code. Required is Python 2.7 or higher, Python packages `h5py`, `mxnet` (for training on CPU) or e.g. `mxnet-cu75` for CUDA 7.5 (for training on GPU with CUDA, concrete package should be selected according to CUDA version). Follow [official instructions on MXNet site](https://mxnet.incubator.apache.org/install/index.html?platform=Linux&language=Python&processor=CPU)
* prediction - generated code is C++. Install MXNet using [official instructions on MXNet site](https://mxnet.incubator.apache.org) for C++.
* Caffe2
* Install Caffe2 using [provided instructions from this link](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/generators/CNNArch2Caffe2#ubuntu).
* training - generated is Python code. Required is Python 2.7
* prediction - generated code is C++.
### HowTo
1. Define a EMADL component containing architecture of a neural network and save it in a `.emadl` file. For more information on architecture language please refer to [CNNArchLang project](https://git.rwth-aachen.de/monticore/EmbeddedMontiArc/languages/CNNArchLang). An example of NN architecture:
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Example of a drivers configuration screen:
![Drivers](doc/torcs_Drivers.png)
3. Use keys `1-9` and `M` to hide all the widgets from the screen
3. Use keys `1-9` and `M` to hide all the widgets such as the speedometer, map, etc. from the TORCS screen
4. Use `F2` key to switch between camera modes to select the mode when the car or it's parts are not visible
5. Use `PgUp/PgDown` keys to switch between cars and select `chenyi` - the car that does not drive on its own
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