https://github.com/tensorlayer/chinese-book
《深度学习：一起玩转TensorLayer》资源分享、讨论
https://github.com/tensorlayer/chinese-book
deep-learning tensorflow tensorlayer
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《深度学习：一起玩转TensorLayer》资源分享、讨论
- Host: GitHub
- URL: https://github.com/tensorlayer/chinese-book
- Owner: tensorlayer
- Created: 2017-12-27T04:28:34.000Z (over 7 years ago)
- Default Branch: master
- Last Pushed: 2018-02-22T15:48:40.000Z (over 7 years ago)
- Last Synced: 2025-04-03T04:12:43.192Z (2 months ago)
- Topics: deep-learning, tensorflow, tensorlayer
- Homepage:
- Size: 1.83 MB
- Stars: 138
- Watchers: 10
- Forks: 47
- Open Issues: 12
-
Metadata Files:
- Readme: README.md
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README
# 《深度学习：一起玩转TensorLayer》资源链接
欢迎补充，感谢！我们也希望更多新的作者加入，请加入[微信群](https://github.com/shorxp/tensorlayer-chinese/blob/master/docs/wechat_group.md)讨论
# 网站资源
* [TensorFlow文档](https://www.tensorflow.org)
* [TensorFlow教程](https://www.tensorflow.org/tutorials/)
* [TensorFlow中文文档 - 极客学院](http://wiki.jikexueyuan.com/project/tensorflow-zh/)
* [TensorLayer英文文档](http://tensorlayer.readthedocs.io/en/latest/)
* [TensorLayer中文文档](http://tensorlayercn.readthedocs.io/zh/latest/)
* [TensorLayer教学例子](https://github.com/zsdonghao/tensorlayer/tree/master/example)
* [TensorLayer深度学习教程](http://tensorlayer.readthedocs.io/en/latest/user/tutorial.html)
* [TensorLayer深度学习教程（中文）](http://tensorlayercn.readthedocs.io/zh/latest/user/tutorial.html)
* [TensorLayer Github](https://github.com/zsdonghao/tensorlayer)
* [《深度学习》网页版 - Goodfellow](http://www.deeplearningbook.org)
* [《深度学习》pdf版 - Goodfellow](https://github.com/zsdonghao/deep-learning-book)
* [Karpathy's Blog](http://karpathy.github.io)
* [Colah's Blog](http://colah.github.io)
* [Wildml](http://www.wildml.com)
* [arXiv](http://arxiv.org)
* [arXiv-sanity](http://www.arxiv-sanity.com)
* [Google Scholar](https://scholar.google.co.uk)
* [TensorLayer使用小技巧－Wagamamaz](https://github.com/wagamamaz/tensorlayer-tricks)
* [GAN Hanks](https://github.com/soumith/ganhacks)
* [斯坦福 CS231N 深度学习框架对比](http://cs231n.stanford.edu/slides/2017/cs231n_2017_lecture8.pdf)
* [TensorFlow 框架对比](http://blog.mdda.net/ai/2016/12/10/layers-on-top-of-tensorflow-review)
# 随书资源与引用链接
* [资源下载](http://www.broadview.com.cn/book/5059)
### 第二章
* [代码－实现手写数字分类](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist_simple.py)
* [代码－通过`all_drop`启动与关闭 Dropout](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mlp_dropout1.py)
* [代码－通过参数共享实现训练测试切换](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mlp_dropout2.py)
* [Neural Networks, Manifolds, and Topology - Colah's Blog](http://colah.github.io/posts/2014-03-NN-Manifolds-Topology/)
* [深度学习中的正则化方法－小毛驴](http://xudongyang.coding.me/regularization-in-deep-learning/)
### 第三章
* [代码－MNIST](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py)
* [Deep Learning, NLP, and Representations - Colah's Blog](http://colah.github.io/posts/2014-07-NLP-RNNs-Representations)
### 第四章
* [代码－MNIST](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_mnist.py)
* [代码－CIFAR10－`tl.prepro`做数据增强](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10.py)
* [代码－CIFAR10－TFRecord做数据增强](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_cifar10_tfrecord.py)
* [代码－CIFAR10－TFRecord做数据增强（TensorFlow官方）](https://www.tensorflow.org/tutorials/deep_cnn)
### 第五章
* [代码－Word2vec](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_word2vec_basic.py)
* [代码－重载预训练矩阵](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_generate_text.py)
* [Word2vec Parameter Learning Explained - Xin Rong](https://arxiv.org/abs/1411.2738)
### 第六章
* [代码－实现生成句子](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_generate_text.py)
* [特朗普演讲数据](https://github.com/zsdonghao/tensorlayer/tree/master/example/data/trump)
* [Recurrent neural network - Wikipedia](https://en.wikipedia.org/wiki/Recurrent_neural_network)
### 第七章
* [代码－乒乓球](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_atari_pong.py)
* [Deep Reinforcement Learning: Pong from Pixels - Karpathy's Blog](http://karpathy.github.io/2016/05/31/rl/)
* [OpenAI Gym](https://gym.openai.com/)
### 第八章
* [代码－DCGAN实现人脸生成](https://github.com/zsdonghao/dcgan)
### 第九章
* [代码－与其它框架对接](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_keras.py)
* [TensorLayer使用小技巧－Wagamamaz](https://github.com/wagamamaz/tensorlayer-tricks)
* [安装NLTK](http://www.nltk.org/install.html)
* [安装NLTK data](http://www.nltk.org/data.html)
* [Jieba分词](https://github.com/fxsjy/jieba)
### 第十章：实例一
* [代码－VGG16](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg16.py)
* [代码－VGG19](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_vgg19.py)
* [代码－Inception V3 与TF-Slim结合](https://github.com/zsdonghao/tensorlayer/blob/master/example/tutorial_inceptionV3_tfslim.py)
* [多伦多大学VGG16下载](http://www.cs.toronto.edu/~frossard/post/vgg16)
* [测试图片下载（data文件夹）](https://github.com/zsdonghao/tensorlayer/tree/master/example/data)
* [TF-Slim预训练模型](https://github.com/tensorflow/models/tree/master/research/slim)
### 第十一章：实例二
* [代码－U-Net脑肿瘤分割](https://github.com/zsdonghao/u-net-brain-tumor)
* [Pascal VOC数据集](http://host.robots.ox.ac.uk/pascal/VOC)
* [从特斯拉到计算机视觉之「图像语义分割」－ 魏秀参](https://zhuanlan.zhihu.com/p/21824299)
### 第十二章：实例三
* [代码－GAN-CLS](https://github.com/zsdonghao/text-to-image)
* [牛津花朵数据集](http://www.robots.ox.ac.uk/~vgg/data/flowers/)
### 第十三章：实例四
* [代码－SRGAN](https://github.com/zsdonghao/SRGAN)
* [Deconvolution and Checkerboard Artifacts - Augustus Odena et al](http://distill.pub/2016/deconv-checkerboard/)
* [DIV2K数据集](http://www.vision.ee.ethz.ch/ntire17)
* [DIV2K上训练的模型](https://github.com/tensorlayer/srgan/releases/tag/1.2.0)
### 第十四章：实例五
* [代码－文本反垃圾](https://github.com/pakrchen/text-antispam)
* [Bazel安装](https://docs.bazel.build/versions/master/install-compile-source.html)
* [TensorFlow Serving](https://github.com/tensorflow/serving)
### 更多实例
* [Github Topic](https://github.com/topics/tensorlayer)
* [TensorLayer Example](http://tensorlayer.readthedocs.io/en/latest/user/example.html)