Microsoft Cognitive Toolkit vs Tensorflow Lite

Microsoft Cognitive Toolkit

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Tensorflow Lite

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Microsoft Cognitive Toolkit vs Tensorflow Lite: 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 Tensorflow Lite? Deploy machine learning models on mobile and IoT devices. It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.

Microsoft Cognitive Toolkit and Tensorflow Lite can be categorized 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, Tensorflow Lite provides the following key features:

  • Lightweight solution for mobile and embedded devices
  • Enables low-latency inference of on-device machine learning models with a small binary size
  • Fast performance

Microsoft Cognitive Toolkit is an open source tool with 16.7K GitHub stars and 4.41K GitHub forks. Here's a link to Microsoft Cognitive Toolkit's open source repository on GitHub.

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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.

What is Tensorflow Lite?

It is a set of tools to help developers run TensorFlow models on mobile, embedded, and IoT devices. It enables on-device machine learning inference with low latency and a small binary size.
What companies use Microsoft Cognitive Toolkit?
What companies use Tensorflow Lite?

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What tools integrate with Microsoft Cognitive Toolkit?
What tools integrate with Tensorflow Lite?
What are some alternatives to Microsoft Cognitive Toolkit and Tensorflow Lite?
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.
Keras
Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
scikit-learn
scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
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.
CUDA
A parallel computing platform and application programming interface model,it enables developers to speed up compute-intensive applications by harnessing the power of GPUs for the parallelizable part of the computation.
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