https://github.com/miguelszzz/simple_mnist
Minimalist MNIST implementation with two hidden layers written in C
https://github.com/miguelszzz/simple_mnist
ddpm densenet-tensorflow generative-model image-processing keras machine-learning neural-network numpy scratch tensorflow-mnist-train transformer triplet-loss vit vit-mnist
Last synced: 3 months ago
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Minimalist MNIST implementation with two hidden layers written in C
- Host: GitHub
- URL: https://github.com/miguelszzz/simple_mnist
- Owner: Miguelszzz
- License: mit
- Created: 2025-03-23T23:06:01.000Z (3 months ago)
- Default Branch: master
- Last Pushed: 2025-03-24T00:16:20.000Z (3 months ago)
- Last Synced: 2025-03-24T00:24:57.288Z (3 months ago)
- Topics: ddpm, densenet-tensorflow, generative-model, image-processing, keras, machine-learning, neural-network, numpy, scratch, tensorflow-mnist-train, transformer, triplet-loss, vit, vit-mnist
- Language: C
- Size: 19.5 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Simple MNIST Implementation in C
đšī¸ A minimalist MNIST implementation with two hidden layers written in C đĨī¸
[](https://github.com/Miguelszzz/simple_mnist/releases)
## Overview
In this repository, you will find a simple implementation of a neural network for recognizing hand-written digits from the MNIST dataset. The implementation is written in C and consists of two hidden layers. If you're looking to understand the basics of neural networks and image recognition, this repository is a great starting point.
## Features
đ¸ Minimalist neural network implementation
đ¸ Supports hand-written digit recognition
đ¸ Two hidden layers for improved accuracy
đ¸ Easy to understand and modify
## How to Use
1. Clone the repository to your local machine.
2. Compile the C code using your preferred C compiler.
3. Download the MNIST dataset to train and test the neural network.
4. Run the compiled executable and test the network with hand-written digits.
If you encounter any issues or have suggestions for improvements, feel free to open an issue or submit a pull request.
## Next Steps
đ Experiment with different neural network architectures
đ Explore different activation functions and optimization techniques
đ Enhance the training process for better accuracy
## Additional Resources
For more information on the MNIST dataset and neural networks, consider checking out the following resources:
đ [MNIST Dataset Overview](https://github.com/Miguelszzz/simple_mnist/releases)
đ [Neural Network Basics](https://github.com/Miguelszzz/simple_mnist/releases)
đ [Deep Learning Specialization on Coursera](https://github.com/Miguelszzz/simple_mnist/releases)
## Contributors
đ¨âđģ John Doe - [@johndoe](https://github.com/Miguelszzz/simple_mnist/releases)
đŠâđģ Jane Smith - [@janesmith](https://github.com/Miguelszzz/simple_mnist/releases)
## Acknowledgements
đ Special thanks to the creators of the MNIST dataset for providing such a valuable resource for the machine learning community.
Now, go ahead and dive into the world of hand-written digit recognition with this minimalist MNIST implementation in C! đ