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ad: Automatic Differentiation

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  Analyzed about 21 hours ago

Forward-, reverse- and mixed- mode automatic differentiation combinators with a common API.

5.75K lines of code

4 current contributors

17 days since last commit

0 users on Open Hub

Very Low Activity
5.0
 
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rad

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  Analyzed about 21 hours ago

Reverse Automatic Differentiation in Haskell

268 lines of code

0 current contributors

over 10 years since last commit

0 users on Open Hub

Inactive
5.0
 
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Licenses: No declared licenses

uncertainties

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  Analyzed about 9 hours ago

Transparent calculations with uncertainties on the quantities involved (aka "error propagation") ; calculation of derivatives.

3.77K lines of code

4 current contributors

14 days since last commit

0 users on Open Hub

Low Activity
0.0
 
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CNTK

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  Analyzed about 21 hours ago

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 ... [More] 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. [Less]

327K lines of code

26 current contributors

over 1 year since last commit

0 users on Open Hub

Very Low Activity
0.0
 
I Use This
Licenses: No declared licenses