Fast Cnn
Generating similiar results of convolution layers from Fast Fourier Transform
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Zhihu | 3,228 | 3 years ago | 31 | Jupyter Notebook | ||||||
| This repo contains the source code in my personal column (https://zhuanlan.zhihu.com/zhaoyeyu), implemented using Python 3.6. Including Natural Language Processing and Computer Vision projects, such as text generation, machine translation, deep convolution GAN and other actual combat code. | ||||||||||
| Sparseconvnet | 1,956 | 4 months ago | 2 | July 10, 2019 | 51 | other | C++ | |||
| Submanifold sparse convolutional networks | ||||||||||
| Grenade | 1,430 | 3 | 5 months ago | 1 | April 12, 2017 | 23 | bsd-2-clause | Haskell | ||
| Deep Learning in Haskell | ||||||||||
| Captainblackboard | 1,047 | a year ago | 85 | mit | C++ | |||||
| 船长关于机器学习、计算机视觉和工程技术的总结和分享 | ||||||||||
| Dl Workshop Series | 935 | 5 months ago | 1 | apache-2.0 | Jupyter Notebook | |||||
| Material used for Deep Learning related workshops for Machine Learning Tokyo (MLT) | ||||||||||
| Conformer | 809 | 5 months ago | 19 | apache-2.0 | Python | |||||
| [Unofficial] PyTorch implementation of "Conformer: Convolution-augmented Transformer for Speech Recognition" (INTERSPEECH 2020) | ||||||||||
| Acnet | 768 | a year ago | 4 | mit | Python | |||||
| ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks | ||||||||||
| Pytorch Inpainting With Partial Conv | 562 | 4 months ago | 8 | mit | Python | |||||
| Unofficial pytorch implementation of 'Image Inpainting for Irregular Holes Using Partial Convolutions' [Liu+, ECCV2018] | ||||||||||
| E2cnn | 534 |  | 2 | 7 months ago | 11 | October 21, 2021 | 7 | other | Python | |
| E(2)-Equivariant CNNs Library for Pytorch | ||||||||||
| Numpycnn | 531 | a year ago | 3 | May 24, 2018 | 1 | Python | ||||
| Building Convolutional Neural Networks From Scratch using NumPy | ||||||||||
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