CNTK VS Recast/Detour

Compare CNTK vs Recast/Detour and see what are their differences.

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CNTK Recast/Detour
1 14
17,435 6,188
0.0% 1.5%
0.0 7.5
about 1 year ago 3 days ago
C++ C++
GNU General Public License v3.0 or later zlib License
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

CNTK

Posts with mentions or reviews of CNTK. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2022-05-20.

Recast/Detour

Posts with mentions or reviews of Recast/Detour. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-04-20.

What are some alternatives?

When comparing CNTK and Recast/Detour you can also consider the following projects:

Theano - Theano was a Python library that allows you to define, optimize, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is being continued as PyTensor: www.github.com/pymc-devs/pytensor

AI-Toolbox - A C++ framework for MDPs and POMDPs with Python bindings

tensorflow - An Open Source Machine Learning Framework for Everyone

Genann - simple neural network library in ANSI C

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration

btsk - Behavior Tree Starter Kit

Caffe - Caffe: a fast open framework for deep learning.

ANNetGPGPU - A GPU (CUDA) based Artificial Neural Network library

Keras - Deep Learning for humans

BayesOpt - BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.

scikit-learn - scikit-learn: machine learning in Python