Pytorch Gan
PyTorch implementations of Generative Adversarial Networks.
| Project Name | Stars | Downloads | Repos Using This | Packages Using This | Most Recent Commit | Total Releases | Latest Release | Open Issues | License | Language |
|---|---|---|---|---|---|---|---|---|---|---|
| Pytorch Gan | 15,262 | 3 months ago | 131 | mit | Python | |||||
| PyTorch implementations of Generative Adversarial Networks. | ||||||||||
| Pytorch Tutorial | 7,372 | a year ago | 28 | mit | Jupyter Notebook | |||||
| Build your neural network easy and fast, 莫烦Python中文教学 | ||||||||||
| Tensorflow Tutorial | 3,873 | 3 years ago | 7 | mit | Python | |||||
| Tensorflow tutorial from basic to hard | ||||||||||
| 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. | ||||||||||
| Alae | 2,850 | 3 years ago | 31 | Python | ||||||
| [CVPR2020] Adversarial Latent Autoencoders | ||||||||||
| Tensorflow Tutorials | 2,123 | a year ago | 23 | Python | ||||||
| 텐서플로우를 기초부터 응용까지 단계별로 연습할 수 있는 소스 코드를 제공합니다 | ||||||||||
| Advanced Deep Learning With Keras | 1,534 | a year ago | 3 | mit | Python | |||||
| Advanced Deep Learning with Keras, published by Packt | ||||||||||
| Niftynet | 1,170 | 4 years ago | 98 | apache-2.0 | Python | |||||
| [unmaintained] An open-source convolutional neural networks platform for research in medical image analysis and image-guided therapy | ||||||||||
| Repo 2017 | 1,146 | 2 years ago | 1 | Python | ||||||
| Python codes in Machine Learning, NLP, Deep Learning and Reinforcement Learning with Keras and Theano | ||||||||||
| Tensorflow2 Generative Models | 833 | 3 years ago | 7 | Jupyter Notebook | ||||||
| Implementations of a number of generative models in Tensorflow 2. GAN, VAE, Seq2Seq, VAEGAN, GAIA, Spectrogram Inversion. Everything is self contained in a jupyter notebook for easy export to colab. | ||||||||||
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