Software
We've compiled a list of 6 free and paid alternatives to The Microsoft Cognitive Toolkit. The primary competitors include Cloud AutoML, Training Mule. In addition to these, users also draw comparisons between The Microsoft Cognitive Toolkit and CatBoost, mlpack, TensorFlow. Also you can look at other similar options here: Development Tools.
We've compiled a list of 6 free and paid alternatives to The Microsoft Cognitive Toolkit. The primary competitors include Cloud AutoML, Training Mule. In addition to these, users also draw comparisons between The Microsoft Cognitive Toolkit and CatBoost, mlpack, TensorFlow. Also you can look at other similar options here: Development Tools.
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Training Mule allows you or your team to easily label images, providing you with the datasets that...
5
CatBoost is an open-source gradient boosting on decision trees library with categorical features...
5
mlpack is a C++ machine learning library with emphasis on scalability, speed, and ease-of-use.
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TensorFlow is an open source software library for machine learning in various kinds of perceptual...
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The Microsoft Cognitive Toolkit - CNTK - is a unified deep-learning toolkit by Microsoft Research.
The Microsoft Cognitive Toolkit - CNTK - is a unified deep-learning toolkit by Microsoft Research.
Windows
Linux
The Microsoft Cognitive Toolkit—previously known as CNTK—empowers you to harness the intelligence within massive datasets through deep learning by providing uncompromised scaling, speed and accuracy with commercial-grade quality and compatibility with the programming languages and algorithms you already use.
It can be included as a library in your Python or C++ programs, or used as a standalone machine learning tool through its own model describtion language (BrainScript).
CNTK supports 64-bit Linux or 64-bit Windows operating systems. To install you can either choose pre-compiled binary packages, or compile the Toolkit from the source provided in Github.
Highly optimized, built-in components
Components can handle multi-dimensional dense or sparse data from Python, C++ or BrainScript
FFN, CNN, RNN/LSTM, Batch normalization, Sequence-to-Sequence with attention and more
Reinforcement learning, generative adversarial networks, supervised and unsupervised learning
Ability to add new user-defined core-components on the GPU from Python
Automatic hyperparameter tuning
Built-in readers optimized for massive datasets
Efficient resource usage
Parallelism with accuracy on multiple GPUs/machines via 1-bit SGD and Block Momentum
Memory sharing and other built-in methods to fit even the largest models in GPU memory
Easily express your own networks
Full APIs for defining networks, learners, readers, training and evaluation from Python, C++ and BrainScript
Evaluate models with Python, C++, C# and BrainScript
Interoperation with NumPy
Both high-level and low-level APIs available for ease of use and flexibility
Automatic shape inference based on your data
Fully optimized symbolic RNN loops (no unrolling needed)
Training and hosting with Azure
Takes advantage of high-speed resources when used with Azure GPU and Azure networks
Host trained models easily on Azure and add real-time training if desired
Artificial intelligence
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