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scratchai is a Deep Learning library that aims to store all Deep Learning algorithms. With easy calls to do all the common tasks in AI.
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README.md

scratchai

Builds

CircleCI

Documentation

Table of Contents:

  1. Classification
Model Paper Implementation Configurations
Lenet http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf Implementation
Alexnet https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf Implementation
VGG https://arxiv.org/pdf/1409.1556.pdf Implementation VGG11, VGG11_BN, VGG13, VGG13_BN, VGG16_BN, VGG19, VGG19_BN, VGG_Dilated (For all the normal configurations)
Resnet https://arxiv.org/abs/1512.03385 Implementation Resnet18, Resnet34, Resnet50, Resnet101, Resnet150, Resnet_dilated (For all the previous resnets)
GoogLeNet https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf Implementation
Resnext https://arxiv.org/abs/1611.05431 NA
  1. Segmentation
Model Paper Implementation
UNet https://arxiv.org/abs/1505.04597 Implementation [Not checked]
ENet https://arxiv.org/abs/1606.02147 Implementation [Not checked]
  1. Generative Adversarial Networks
Model Paper Implementation
DCGAN https://arxiv.org/abs/1511.06434 NA
CycleGAN https://arxiv.org/abs/1703.10593 Implementation [Not checked]
  1. Style Transfer
Model Paper Implementation
Image Transformation Network Justin et al. Perceptual Losses Paper
Supplementary Material
Implementation
  1. Attacks
Attacks Paper Implementation
Noise NA Implementation
Semantic https://arxiv.org/abs/1703.06857 Implementation
Saliency Map Method https://arxiv.org/pdf/1511.07528.pdf Ongoing
Fast Gradient Method https://arxiv.org/abs/1412.6572 Implementation
Projected Gradient Descent https://arxiv.org/pdf/1607.02533.pdf
https://arxiv.org/pdf/1706.06083.pdf
Implementation
DeepFool https://arxiv.org/abs/1511.04599 pdf Implementation

Tutorials

Tutorials on how to get the most out of scratchai can be found here: https://github.com/iArunava/scratchai/tree/master/tutorials

These are ongoing list of tutorials and scratchai is looking for more and more contributions. If you are willing to contribute please take a look at the CONTRIBUTING.md / open a issue.

License

The code under this repository is distributed under MIT License. Feel free to use it in your own work with proper citations to this repository.

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