lnn
autodiff
| lnn | autodiff | |
|---|---|---|
| 5 | 6 | |
| 20 | 36 | |
| - | - | |
| 6.3 | 3.6 | |
| 7 months ago | 10 months ago | |
| C | C | |
| BSD 3-clause "New" or "Revised" License | MIT License |
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lnn
- LNN - A CLI Program of Neural Networks Written in C
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I've made a command-line program of neural networks in C
The code and documentation are hosted at GitHub https://github.com/dongyx/lnn.
- Show HN: Training Neural Networks with a Single Unix Pipeline
- A Command-line Program of Neural Networks Written in C
- LNN: Training and Running Neural Networks in Command-line, Implemented in C
autodiff
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A Command-line Program of Neural Networks Written in C
Nice work! I would recommend to look into implementing automatic differentiation so that the tool is easily extendible to any differentiable function. For inspiration, I made a scalar valued autodiff implementation a while back repo found here. Making a vector valued implementation would not be too difficult and would be more in line with tensorflow/pytorch api
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A C++ version of Micrograd!
Very nice implementation! I recently did an autodiff implementation myself in C. So it is interesting to see how you did it in C++. If you wanna take a look at my autodiff implementation, here is the repo.
- Automatic differentiation in C
- [Continued] Autodiff implementation in C
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Autodiff in C (need help fixing memory leak)
Anyways, here is the repo: Autodiff
What are some alternatives?
ggml - Tensor library for machine learning
cpp-micrograd - A c/c++ implementation of micrograd: a tiny autograd engine with neural net on top.
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stan - Stan development repository. The master branch contains the current release. The develop branch contains the latest stable development. See the Developer Process Wiki for details.
Nerve - This is a basic implementation of a neural network for use in C and C++ programs. It is intended for use in applications that just happen to need a simple neural network and do not want to use needlessly complex neural network libraries.
ruby-fann - Ruby library for interfacing with FANN (Fast Artificial Neural Network)
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