Caffe vs Microsoft Cognitive Toolkit

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Caffe

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Microsoft Cognitive Toolkit

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Microsoft Cognitive Toolkit vs Caffe: What are the differences?

What is Microsoft Cognitive Toolkit? An open-source toolkit for deep learning. It is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph.

What is Caffe? A deep learning framework. It is a deep learning framework made with expression, speed, and modularity in mind.

Microsoft Cognitive Toolkit and Caffe can be primarily classified as "Machine Learning" tools.

Some of the features offered by Microsoft Cognitive Toolkit are:

  • Speed & Scalability
  • Commercial-Grade Quality
  • Easy-to-use architecture

On the other hand, Caffe provides the following key features:

  • Extensible code
  • Speed
  • Community

Microsoft Cognitive Toolkit and Caffe are both open source tools. It seems that Caffe with 29.2K GitHub stars and 17.6K forks on GitHub has more adoption than Microsoft Cognitive Toolkit with 16.5K GitHub stars and 4.39K GitHub forks.

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What is Caffe?

It is a deep learning framework made with expression, speed, and modularity in mind.

What is Microsoft Cognitive Toolkit?

It is an open-source toolkit for commercial-grade distributed deep learning. It describes neural networks as a series of computational steps via a directed graph.

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What companies use Caffe?
What companies use Microsoft Cognitive Toolkit?
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What tools integrate with Caffe?
What tools integrate with Microsoft Cognitive Toolkit?

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What are some alternatives to Caffe and Microsoft Cognitive Toolkit?
TensorFlow
TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
Torch
It is easy to use and efficient, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.
PyTorch
PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
Caffe2
Caffe2 is deployed at Facebook to help developers and researchers train large machine learning models and deliver AI-powered experiences in our mobile apps. Now, developers will have access to many of the same tools, allowing them to run large-scale distributed training scenarios and build machine learning applications for mobile.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
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