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https://github.com/miladsade96/diagtomodel

Implementations of Artificial Neural Networks Based on their Diagrams
https://github.com/miladsade96/diagtomodel

artificial-intelligence cnn computer-vision convolutional-neural-networks deep-learning deep-neural-networks hacktoberfest2021 keras machine-learning models neural-network python tensorflow

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Implementations of Artificial Neural Networks Based on their Diagrams

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### Implementations of Artificial Neural Networks Based on their Diagrams

## Authors
* Milad Sadeghi DM - [EverlookNeverSee@GitHub](https://github.com/EverLookNeverSee)
* List of [all contributors](https://github.com/EverLookNeverSee/diagtomodel/graphs/contributors) to this repository

## Implemented Models
* [PLNet](diagtomodel/pl_net/pl_net.py) - Convolutional Neural Network with Parallel Layers
* [MLANet](diagtomodel/mla_net/mla_net.py) - Convolutional Neural Network with Multiple Layer Additions
* [LeNet-5](diagtomodel/lenet_5/lenet-5.py) - Gradient-Based Learning Applied to Document Recognition
* [AlexNet](diagtomodel/alexnet/alexnet.py) - ImageNet Classification with Deep Convolutional
Neural Networks
* [VGG-16](diagtomodel/vgg_16/vgg-16.py) - Very Deep Convolutional Networks For Large Scale Image Recognition
* [Inception-v1](diagtomodel/inception_v1/inception-v1.py) - Going Deeper With Convolutions
* [Xception](diagtomodel/xception/xception.py) - Deep Learning with Depthwise Separable Convolutions

## License
This project licensed under the MIT License - see the [LICENSE](LICENSE) file for more details.