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Project Summary

CNTK (Computational Network Toolkit) by Microsoft Research, is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph. In this directed graph, leaf nodes represent input values or network parameters, while other nodes represent matrix operations upon their inputs. CNTK allows to easily realize and combine popular model types such as feed-forward DNNs, convolutional nets (CNNs), and recurrent networks (RNNs/LSTMs). It implements stochastic gradient descent (SGD, error backpropagation) learning with automatic differentiation and parallelization across multiple GPUs and servers.

Tags

automatic_differentiation cnn convolutional_networks deeplearning deep_learning distributed_computing dnn error_backpropagation feedforward GPU GPU_cluster lstm multi_CPU multi_GPU parallelization recurrent_networks rnn sgd stochastic_gradient_descent

In a Nutshell, CNTK...

Project Security

Vulnerabilities per Version ( last 10 releases )

There are no reported vulnerabilities

Project Vulnerability Report

Security Confidence Index

Poor security track-record
Favorable security track-record

Vulnerability Exposure Index

Many reported vulnerabilities
Few reported vulnerabilities

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Languages

Languages?height=75&width=75
C++
60%
Python
20%
XML
12%
15 Other
8%

30 Day Summary

Mar 20 2021 — Apr 19 2021

12 Month Summary

Apr 19 2020 — Apr 19 2021
  • 0 Commits
    Down -30 (100%) from previous 12 months
  • 0 Contributors
    Down -6 (100%) from previous 12 months

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