frugally-deep
Genann
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frugally-deep | Genann | |
---|---|---|
5 | 7 | |
1,043 | 1,905 | |
- | - | |
8.0 | 0.0 | |
6 days ago | 8 months ago | |
C++ | C | |
MIT License | zlib License |
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frugally-deep
- Frugally-deep: Header-only library for using Keras (TensorFlow) models in C++
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Ask HN: What side projects landed you a job?
The interview for my current job first went mediocre, but by talking about frugally-deep (a side project of mine) I was able to excite my (now) employer. :-)
https://github.com/Dobiasd/frugally-deep
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Way to convert keras based model to C++.
Did you work through the FAQ here: https://github.com/Dobiasd/frugally-deep/blob/master/FAQ.md
- Handwritten digit recognition using CircuitPython, Raspberry Pi Pico, OV7670 and 120x160 TFT LCD. r/circuitpython - Handwritten digit recognition using CircuitPython, Raspberry Pi Pico, OV7670 and 120x160 TFT LCD.
Genann
- Simple neural network library in ANSI C
- Genann: Simple neural network library in ANSI C
- Machine learning Library in C?
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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
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C Deep
Genann - Simple ANN in C89, without additional dependencies. Zlib
What are some alternatives?
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
tiny-cnn - header only, dependency-free deep learning framework in C++14
tensorflow - An Open Source Machine Learning Framework for Everyone
Recast/Detour - Industry-standard navigation-mesh toolset for games
Taskflow - A General-purpose Parallel and Heterogeneous Task Programming System
ANNetGPGPU - A GPU (CUDA) based Artificial Neural Network library
nano
BayesOpt - BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.
Native System Automation - Native cross-platform system automation
CNTK - Microsoft Cognitive Toolkit (CNTK), an open source deep-learning toolkit