2019-01-22 21:30:39

docs/1.0/SUMMARY.md

 +   中文教程
     +   [Getting Started](tut_getting_started.md)
-        +   [Deep Learning with PyTorch: A 60 Minute Blitz](deep_learning_60min_blitz.md)
-            + [What is PyTorch?](blitz_tensor_tutorial.md)
-            + [Autograd: Automatic Differentiation](blitz_autograd_tutorial.md)
-            + [Neural Networks](blitz_neural_networks_tutorial.md)
-            + [Training a Classifier](blitz_cifar10_tutorial.md)
-            + [Optional: Data Parallelism](blitz_data_parallel_tutorial.md)
+        +   [PyTorch 深度学习: 60 分钟极速入门](deep_learning_60min_blitz.md)
+            + [什么是 PyTorch？](blitz_tensor_tutorial.md)
+            + [Autograd：自动求导](blitz_autograd_tutorial.md)
+            + [神经网络](blitz_neural_networks_tutorial.md)
+            + [训练分类器](blitz_cifar10_tutorial.md)
+            + [可选：数据并行处理](blitz_data_parallel_tutorial.md)
         +   [Data Loading and Processing Tutorial](data_loading_tutorial.md)
-        +   [Learning PyTorch with Examples](pytorch_with_examples.md)
+        +   [用例子学习 PyTorch](pytorch_with_examples.md)
         +   [Transfer Learning Tutorial](transfer_learning_tutorial.md)
         +   [Deploying a Seq2Seq Model with the Hybrid Frontend](deploy_seq2seq_hybrid_frontend_tutorial.md)
         +   [Saving and Loading Models](saving_loading_models.md)

docs/1.0/blitz_cifar10_tutorial.md

@@ -20,7 +20,7 @@
 
 在这个教程中，我们将使用CIFAR10数据集，它有如下的分类：“飞机”，“汽车”，“鸟”，“猫”，“鹿”，“狗”，“青蛙”，“马”，“船”，“卡车”等。在CIFAR-10里面的图片数据大小是3x32x32，即三通道彩色图，图片大小是32x32像素。
 
-![cifar10](../Images/ae800707f2489607d51d67499071db16.jpg)
+![cifar10](img/ae800707f2489607d51d67499071db16.jpg)
 
 ## 训练一个图片分类器
 
@@ -94,7 +94,7 @@ imshow(torchvision.utils.make_grid(images))
 print(' '.join('%5s' % classes[labels[j]] for j in range(4)))
 
 ```
-![https://pytorch.org/tutorials/_images/sphx_glr_cifar10_tutorial_001.png](../Images/aaf8c905effc5044cb9691420e5261fa.jpg)
+![https://pytorch.org/tutorials/_images/sphx_glr_cifar10_tutorial_001.png](img/aaf8c905effc5044cb9691420e5261fa.jpg)
 
 输出：
 
@@ -209,7 +209,7 @@ imshow(torchvision.utils.make_grid(images))
 print('GroundTruth: ', ' '.join('%5s' % classes[labels[j]] for j in range(4)))
 ```
 
-![https://pytorch.org/tutorials/_images/sphx_glr_cifar10_tutorial_002.png](../Images/d148a5bd51a3278e9698bba522cbc34a.jpg)
+![https://pytorch.org/tutorials/_images/sphx_glr_cifar10_tutorial_002.png](img/d148a5bd51a3278e9698bba522cbc34a.jpg)
 
 ```python
 GroundTruth:    cat  ship  ship plane

docs/1.0/blitz_neural_networks_tutorial.md

@@ -8,7 +8,7 @@
 
 例如，下面这个神经网络可以对数字进行分类：
 
-![convnet](../Images/3250cbba812d68265cf7815d987bcd1b.jpg)
+![convnet](img/3250cbba812d68265cf7815d987bcd1b.jpg)
 
 这是一个简单的前馈神经网络（feed-forward network）。它接受一个输入，然后将它送入下一层，一层接一层的传递，最后给出输出。