OpenNARS-for-Applications
Genann
OpenNARS-for-Applications | Genann | |
---|---|---|
1 | 7 | |
86 | 1,911 | |
- | - | |
7.5 | 0.0 | |
26 days ago | 8 months ago | |
C | C | |
MIT License | zlib License |
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.
OpenNARS-for-Applications
-
From here to proto-AGI: what might it take and what might happen
NARS ( https://github.com/opennars/OpenNARS-for-Applications )
Genann
- Simple neural network library in ANSI C
- Genann: Simple neural network library in ANSI C
- Machine learning Library in C?
-
Ask HN: What ML platform are you using?
> I am very much a beginner in the space of machine learning
While the (precious and useful) advice around seem to cover mostly the bigger infrastructures, please note that
you can effectively do an important slice of machine learning work (study, personal research) with just a battery-efficiency-level CPU (not GPU), in the order of minutes, on a battery. That comes before going to "Big Data".
And there are lightweight tools: I am current enamoured with Genann («minimal, well-tested open-source library implementing feedfordward artificial neural networks (ANN) in C»), a single C file of 400 lines compiling to a 40kb object, yet well sufficient to solve a number of the problems you may meet.
https://codeplea.com/genann // https://github.com/codeplea/genann
After all, is it a good idea to have tools that automate process optimization while you are learning the deal? Only partially. You should build - in general and even metaphorically - the legitimacy of your Python ops on a good C ground.
And: note that you can also build ANNs in R (and other math or stats environments). If needed or comfortable...
Also note - reminder - that the MIT lessons of Prof. Patrick Winston for the Artificial Intelligence course (classical AI with a few lessons on ANNs) are freely available. That covers the grounds relative to climb into the newer techniques.
- Small tensor library in C99
-
C Deep
Genann - Simple ANN in C89, without additional dependencies. Zlib
What are some alternatives?
opennars - OpenNARS for Research 3.0+
tiny-cnn - header only, dependency-free deep learning framework in C++14
phoebe - Phoebe
Recast/Detour - Industry-standard navigation-mesh toolset for games
lab - A customisable 3D platform for agent-based AI research
frugally-deep - A lightweight header-only library for using Keras (TensorFlow) models in C++.
MV-Tractus - A simple tool to extract motion vectors from h264 encoded videos.
tensorflow - An Open Source Machine Learning Framework for Everyone
ruby-fann - Ruby library for interfacing with FANN (Fast Artificial Neural Network)
ANNetGPGPU - A GPU (CUDA) based Artificial Neural Network library
BayesOpt - BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.
CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit