Use Cases

• Deep Learning Development CNTK can be used as a library in Python, C#, or C++ programs for developing deep learning models. It allows users to easily realize and combine popular model types such as feed-forward DNNs, convolutional neural networks (CNNs), and recurrent neural networks (RNNs/LSTMs).
• Standalone Machine Learning Tool CNTK can also be used as a standalone machine-learning tool through its model description language, BrainScript. This makes it a useful tool for those who want to develop machine learning models without having to write code in Python, C#, or C++.
• Model Evaluation The CNTK model evaluation functionality can be used from Java programs. This makes it a useful tool for those who want to evaluate their deep learning models using Java.
• ONNX Support CNTK is one of the first deep-learning toolkits to support the Open Neural Network Exchange ONNX format. This makes it a useful tool for those who want to develop models that can be easily shared across different deep learning frameworks.
• 1-bit Stochastic Gradient Descent There is no separate license required to use the 1-bit Stochastic Gradient Descent (1-bit SGD) in CNTK. This makes it a useful tool for those who want to use this specific feature without having to pay for a separate license.