This folder contains examples using CNTK C# API to build, train, and evaluate CNTK neural network models.

Data and model preparation are done with python scripts.

To install CIFAR10 dataset, change directory to Examples\Image\DataSets\CIFAR-10, run:

python install_cifar10.py 

To install VGG flower and animal data and to download the ResNet model, change directory to Examples\Image\TransferLearning, run:

python install_data_and_model.py

1. Install Nuget package CNTK.CPUOnly version v2.2.0 or higher for CSTrainingCPUOnlyExamples.
2. Install Nuget package CNTK.GPU version v2.2.0 or higher for CSTrainingGPUExamples
3. Run following examples:

A hello-world example to train and evaluate a logistic regression model using C#/API. See CNTK 101: Logistic Regression and ML Primer for more details.

This class shows how to build and train a classifier for handwriting data (MNIST).

This class shows how to do image classification using ResNet. The model being built is a lite version of Deep Residual Learning for Image Recognition. See Python Tutorials for more details.

This class demonstrates transfer learning using a pretrained ResNet model. See Python Tutorials for more details.

This class shows how to build a recurrent neural network model from ground up and how to train the model.