Recast/Detour VS CNTK

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

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Recast/Detour CNTK
14 1
6,222 17,435
1.1% 0.0%
7.4 0.0
8 days ago about 1 year ago
C++ C++
zlib License GNU General Public License v3.0 or later
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.

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.

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.

What are some alternatives?

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

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

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

Genann - simple neural network library in ANSI C

tensorflow - An Open Source Machine Learning Framework for Everyone

btsk - Behavior Tree Starter Kit

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

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

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

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

Keras - Deep Learning for humans

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