CNTK also offers several examples that are not in Tutorial style. Many of these are recipes involve more advanced networks and are located under Examples directory.
- MNIST: A fully connected feed-forward model for classification of MNIST images. (Follow the instructions in Examples/Image/DataSets/MNIST/README.md to get the MNIST data set)
- TrainResNet_CIFAR10: An image classification ResNet model for training on the CIFAR image dataset. (Follow the instructions in Examples/Image/DataSets/CIFAR-10/README.md to get the CIFAR dataset and convert it to the CNTK supported format)
- SequenceClassification: An LSTM sequence classification model for text data.
- Sequence2Sequence: A sequence to sequence grapheme to phoneme translation model that trains on the CMUDict corpus.
- NumpyInterop - Language Understanding.
- Video - Basic 3D convolution networks for deep learning on video tasks.