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Keras

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  Analyzed 5 months ago

Deep Learning library for Python. Convnets, recurrent neural networks, and more. Keras is a minimalist, highly modular neural networks library, written in Python and capable of running either on top of either TensorFlow or Theano. It was developed with a focus on enabling fast experimentation. ... [More] Use Keras if you need a deep learning library that: - allows for easy and fast prototyping (through total modularity, minimalism, and extensibility). - supports both convolutional networks and recurrent networks, as well as combinations of the two. - supports arbitrary connectivity schemes (including multi-input and multi-output training). - runs seamlessly on CPU and GPU. [Less]

159K lines of code

122 current contributors

5 months since last commit

7 users on Open Hub

Activity Not Available
0.0
 
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delira

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  Analyzed 1 day ago

Lightweight framework for fast prototyping and training deep neural networks with PyTorch and TensorFlow

13.7K lines of code

12 current contributors

over 2 years since last commit

1 users on Open Hub

Inactive
0.0
 
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studio-go-runner

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  Analyzed about 4 hours ago

ML/ENN Runner for privately hosted, cloud, and data-center deployments of StudioML (Beta)

965K lines of code

5 current contributors

almost 2 years since last commit

1 users on Open Hub

Very Low Activity
5.0
 
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Licenses: No declared licenses

LIBXSMM

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  Analyzed 2 months ago

Library for specialized dense and sparse matrix operations, and deep learning primitives.

210K lines of code

8 current contributors

2 months since last commit

1 users on Open Hub

Activity Not Available
5.0
 
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PhotoPrism

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  Analyzed about 8 hours ago

Personal photo management powered by Go and Google TensorFlow. Free and open-source. Made with ❤️ in Berlin.

173K lines of code

12 current contributors

1 day since last commit

1 users on Open Hub

Very High Activity
0.0
 
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zfit

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  Analyzed about 3 hours ago

scalable pythonic model fitting for High Energy Physics

30.3K lines of code

10 current contributors

about 1 month since last commit

0 users on Open Hub

Moderate Activity
0.0
 
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MocapNET

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  Analyzed about 18 hours ago

We present MocapNET, an ensemble of SNN encoders that estimates the 3D human body pose based on 2D joint estimations extracted from monocular RGB images. MocapNET provides BVH file output which can be rendered in real-time or imported without any additional processing in most popular 3D animation ... [More] software. The proposed architecture achieves 3D human pose estimations at state of the art rates of 400Hz using only CPU processing. [Less]

32.9K lines of code

0 current contributors

22 days since last commit

0 users on Open Hub

Very Low Activity
0.0
 
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Licenses: No declared licenses