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Tutorial_10 - [transfer learning] Add new variables to graph and save the new model.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_10%20%20-%20%5Btransfer%20learning%5D%20Add%20new%20variables%20to%20graph%20and%20save%20the%20new%20model.ipynb
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Tutorial_12 - [tfrecord] use tfrecord to store sequences of different length.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_12%20-%20%5Btfrecord%5D%20use%20tfrecord%20to%20store%20sequences%20of%20different%20length.ipynb
Tutorial_13 - Basic Seq2seq example.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_13%20-%20Basic%20Seq2seq%20example.ipynb
Tutorial_13 - Basic Seq2seq example.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_13%20-%20Basic%20Seq2seq%20example.ipynb
Tutorial_2 - A simple feedforward network for MNIST.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_2%20-%20%20A%20simple%20feedforward%20network%20for%20MNIST.ipynb
Tutorial_2 - A simple feedforward network for MNIST.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_2%20-%20%20A%20simple%20feedforward%20network%20for%20MNIST.ipynb
Tutorial_3 - Convolutional network for MNIST.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_3%20-%20Convolutional%20network%20for%20MNIST.ipynb
Tutorial_3 - Convolutional network for MNIST.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_3%20-%20Convolutional%20network%20for%20MNIST.ipynb
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Tutorial_4 - The usage of name_variable_scope.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_4%20-%20The%20usage%20of%20%20name_variable_scope.ipynb
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Tutorial_5 - An understandable example to implement Multi-LSTM for MNIST.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_5%20-%20An%20understandable%20example%20to%20implement%20Multi-LSTM%20for%20MNIST.ipynb
Tutorial_5 - An understandable example to implement Multi-LSTM for MNIST.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_5%20-%20An%20understandable%20example%20to%20implement%20Multi-LSTM%20for%20MNIST.ipynb
Tutorial_5.2 - An understandable example to implement Multi-GRU for MNIST.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_5.2%20-%20An%20understandable%20example%20to%20implement%20Multi-GRU%20for%20MNIST.ipynb
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Tutorial_6 - Bi-directional LSTM for sequence labeling (Chinese segmentation).ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_6%20-%20Bi-directional%20LSTM%20for%20sequence%20labeling%20(Chinese%20segmentation).ipynb
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Tutorial_8 - A very simple example for tensorboard.ipynbhttps://patch-diff.githubusercontent.com/jackx2006/Tensorflow-Tutorial/blob/master/Tutorial_8%20-%20A%20very%20simple%20example%20for%20tensorboard.ipynb
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TensorFlow入门（一）基本用法http://blog.csdn.net/jerr__y/article/details/57084008
TensorFlow入门（二）简单前馈网络实现 mnist 分类http://blog.csdn.net/jerr__y/article/details/57084077
TensorFlow入门（三）多层 CNNs 实现 mnist分类http://blog.csdn.net/jerr__y/article/details/57086434
TensorFlow入门（四） name / variable_scope 的使用http://blog.csdn.net/jerr__y/article/details/60877873
TensorFlow入门（五）多层 LSTM 通俗易懂版http://blog.csdn.net/jerr__y/article/details/61195257
TensorFlow入门（六）双端 LSTM 实现序列标注（分词）http://blog.csdn.net/jerr__y/article/details/70471066
TensorFlow入门（七）充分理解 name / variable_scopehttp://blog.csdn.net/Jerr__y/article/details/70809528
Tensorflow入门（十一） 【模型联合】如何利用tf.train.saver()把多个预训练好的模型联合起来fine-tunehttps://github.com/yongyehuang/zhihu-text-classification/blob/master/models/Using%20saver%20to%20combine%20models.ipynb
Tensorflow入门（十二）使用 tfrecord 读取数据https://github.com/yongyehuang/Tensorflow-Tutorial/tree/master/python/the_use_of_tfrecord
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