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[example] vae for mnist
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README.md

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@@ -66,7 +66,8 @@ Examples can be found [in this folder](https://github.com/zsdonghao/tensorlayer/
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- [Spatial Transformer Networks](https://arxiv.org/abs/1506.02025) by [zsdonghao](https://github.com/zsdonghao/Spatial-Transformer-Nets).
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- [U-Net for brain tumor segmentation](https://github.com/zsdonghao/u-net-brain-tumor) by [zsdonghao](https://github.com/zsdonghao/u-net-brain-tumor).
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- Variational Autoencoder (VAE) for (CelebA) by [yzwxx](https://github.com/yzwxx/vae-celebA).
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- Image Captioning - Reimplementation of Google's [im2txt](https://github.com/tensorflow/models/tree/master/research/im2txt) by [zsdonghao](https://github.com/zsdonghao/Image-Captioning).
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- Variational Autoencoder (VAE) for (MNIST) by [BUPTLdy](https://github.com/BUPTLdy/tl-vae).
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- Image Captioning - Reimplementation of Google's [im2txt](https://github.com/tensorflow/models/tree/master/research/im2txt) by [zsdonghao](https://github.com/zsdonghao/Image-Captioning).
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## Natural Language Processing
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- Recurrent Neural Network (LSTM). Apply multiple LSTM to PTB dataset for language modeling, see [tutorial_ptb_lstm.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_ptb_lstm.py) and [tutorial\_ptb\_lstm\_state\_is_tuple.py](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_ptb_lstm_state_is_tuple.py).

docs/user/example.rst

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- `Spatial Transformer Networks <https://arxiv.org/abs/1506.02025>`_ by `zsdonghao <https://github.com/zsdonghao/Spatial-Transformer-Nets>`_.
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- `U-Net for brain tumor segmentation <https://github.com/zsdonghao/u-net-brain-tumor>`_ by `zsdonghao <https://github.com/zsdonghao/u-net-brain-tumor>`_.
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- Variational Autoencoder (VAE) for (CelebA) by `yzwxx <https://github.com/yzwxx/vae-celebA>`_.
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- Variational Autoencoder (VAE) for (MNIST) by `BUPTLdy <https://github.com/BUPTLdy/tl-vae>`_.
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- Image Captioning - Reimplementation of Google's `im2txt <https://github.com/tensorflow/models/tree/master/research/im2txt>`_ by `zsdonghao <https://github.com/zsdonghao/Image-Captioning>`_.
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Natural Language Processing

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