diff --git a/doc/paddle/index_cn.rst b/doc/paddle/index_cn.rst
index 8132b78798d527b39afdeaaf0ee0a6f61f61aede..de09e58d9c1124033d7375d939d8f126acb1c18e 100644
--- a/doc/paddle/index_cn.rst
+++ b/doc/paddle/index_cn.rst
@@ -13,6 +13,7 @@
 
     install/index_cn.rst
     guides/index_cn.rst
+    tutorial/index_cn.rst
     api/index_cn.rst
     faq/index_cn.rst
     release_note_cn.md
diff --git a/doc/paddle/tutorial/cv_case/index_cn.rst b/doc/paddle/tutorial/cv_case/index_cn.rst
new file mode 100644
index 0000000000000000000000000000000000000000..2bf44a2bde89c7f01bc25b9df76e6cf6ff4b545e
--- /dev/null
+++ b/doc/paddle/tutorial/cv_case/index_cn.rst
@@ -0,0 +1,15 @@
+################
+计算机视觉
+################
+
+
+在这里PaddlePaddle为大家提供了一篇cv的教程供大家学习：
+    
+     - `图像分类 <./mnist_lenet_classification/mnist_lenet_classification.html>`_ ：介绍使用 Paddle 在MNIST数据集上完成图像分类。
+    
+..  toctree::
+    :hidden:
+    :titlesonly:
+
+    mnist_lenet_classification/mnist_lenet_classification.rst
+
diff --git a/doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification.rst b/doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification.rst
new file mode 100644
index 0000000000000000000000000000000000000000..0a6e138547294f2f8e9048b392d9667830155a5d
--- /dev/null
+++ b/doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification.rst
@@ -0,0 +1,503 @@
+MNIST数据集使用LeNet进行图像分类
+================================
+
+本示例教程演示如何在MNIST数据集上用LeNet进行图像分类。
+手写数字的MNIST数据集，包含60,000个用于训练的示例和10,000个用于测试的示例。这些数字已经过尺寸标准化并位于图像中心，图像是固定大小(28x28像素)，其值为0到1。该数据集的官方地址为：http://yann.lecun.com/exdb/mnist/
+
+环境
+----
+
+本教程基于paddle-develop编写，如果您的环境不是本版本，请先安装paddle-develop版本。
+
+.. code:: ipython3
+
+    import paddle
+    print(paddle.__version__)
+    paddle.disable_static()
+
+
+.. parsed-literal::
+
+    0.0.0
+
+
+加载数据集
+----------
+
+我们使用飞桨自带的paddle.dataset完成mnist数据集的加载。
+
+.. code:: ipython3
+
+    print('download training data and load training data')
+    train_dataset = paddle.vision.datasets.MNIST(mode='train')
+    test_dataset = paddle.vision.datasets.MNIST(mode='test')
+    print('load finished')
+
+
+.. parsed-literal::
+
+    /Library/Python/3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
+      and should_run_async(code)
+
+
+.. parsed-literal::
+
+    download training data and load training data
+    load finished
+
+
+取训练集中的一条数据看一下。
+
+.. code:: ipython3
+
+    import numpy as np
+    import matplotlib.pyplot as plt
+    train_data0, train_label_0 = train_dataset[0][0],train_dataset[0][1]
+    train_data0 = train_data0.reshape([28,28])
+    plt.figure(figsize=(2,2))
+    plt.imshow(train_data0, cmap=plt.cm.binary)
+    print('train_data0 label is: ' + str(train_label_0))
+
+
+.. parsed-literal::
+
+    train_data0 label is: [5]
+
+
+.. image:: https://github.com/PaddlePaddle/FluidDoc/blob/develop/doc/paddle/user_guides/cv_case/image_classification/image/cifar.png?raw=true
+
+2.组网
+------
+
+用paddle.nn下的API，如\ ``Conv2d``\ 、\ ``Pool2D``\ 、\ ``Linead``\ 完成LeNet的构建。
+
+.. code:: ipython3
+
+    import paddle
+    import paddle.nn.functional as F
+    class LeNet(paddle.nn.Layer):
+        def __init__(self):
+            super(LeNet, self).__init__()
+            self.conv1 = paddle.nn.Conv2d(in_channels=1, out_channels=6, kernel_size=5, stride=1, padding=2)
+            self.max_pool1 = paddle.nn.MaxPool2d(kernel_size=2,  stride=2)
+            self.conv2 = paddle.nn.Conv2d(in_channels=6, out_channels=16, kernel_size=5, stride=1)
+            self.max_pool2 = paddle.nn.MaxPool2d(kernel_size=2, stride=2)
+            self.linear1 = paddle.nn.Linear(in_features=16*5*5, out_features=120)
+            self.linear2 = paddle.nn.Linear(in_features=120, out_features=84)
+            self.linear3 = paddle.nn.Linear(in_features=84, out_features=10)
+    
+        def forward(self, x):
+            x = self.conv1(x)
+            x = F.relu(x)
+            x = self.max_pool1(x)
+            x = F.relu(x)
+            x = self.conv2(x)
+            x = self.max_pool2(x)
+            x = paddle.reshape(x, shape=[-1, 16*5*5])
+            x = self.linear1(x)
+            x = F.relu(x)
+            x = self.linear2(x)
+            x = F.relu(x)
+            x = self.linear3(x)
+            x = F.softmax(x)
+            return x
+
+
+.. parsed-literal::
+
+    /Library/Python/3.7/site-packages/ipykernel/ipkernel.py:287: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.
+      and should_run_async(code)
+
+
+3.训练方式一
+------------
+
+组网后，开始对模型进行训练，先构建\ ``train_loader``\ ，加载训练数据，然后定义\ ``train``\ 函数，设置好损失函数后，按batch加载数据，完成模型的训练。
+
+.. code:: ipython3
+
+    import paddle
+    train_loader = paddle.io.DataLoader(train_dataset, places=paddle.CPUPlace(), batch_size=64)
+    # 加载训练集 batch_size 设为 64
+    def train(model):
+        model.train()
+        epochs = 2
+        optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
+        # 用Adam作为优化函数
+        for epoch in range(epochs):
+            for batch_id, data in enumerate(train_loader()):
+                x_data = data[0]
+                y_data = data[1]
+                predicts = model(x_data)
+                loss = paddle.nn.functional.cross_entropy(predicts, y_data)
+                # 计算损失
+                acc = paddle.metric.accuracy(predicts, y_data, k=2)
+                avg_loss = paddle.mean(loss)
+                avg_acc = paddle.mean(acc)
+                avg_loss.backward()
+                if batch_id % 100 == 0:
+                    print("epoch: {}, batch_id: {}, loss is: {}, acc is: {}".format(epoch, batch_id, avg_loss.numpy(), avg_acc.numpy()))
+                optim.minimize(avg_loss)
+                model.clear_gradients()
+    model = LeNet()
+    train(model)
+
+
+.. parsed-literal::
+
+    epoch: 0, batch_id: 0, loss is: [2.3062382], acc is: [0.109375]
+    epoch: 0, batch_id: 100, loss is: [1.6826601], acc is: [0.84375]
+    epoch: 0, batch_id: 200, loss is: [1.685574], acc is: [0.796875]
+    epoch: 0, batch_id: 300, loss is: [1.5752499], acc is: [0.96875]
+    epoch: 0, batch_id: 400, loss is: [1.5006541], acc is: [1.]
+    epoch: 0, batch_id: 500, loss is: [1.5343401], acc is: [0.984375]
+    epoch: 0, batch_id: 600, loss is: [1.4875913], acc is: [0.984375]
+    epoch: 0, batch_id: 700, loss is: [1.5139006], acc is: [0.984375]
+    epoch: 0, batch_id: 800, loss is: [1.5227785], acc is: [0.984375]
+    epoch: 0, batch_id: 900, loss is: [1.4938308], acc is: [1.]
+    epoch: 1, batch_id: 0, loss is: [1.4826943], acc is: [0.984375]
+    epoch: 1, batch_id: 100, loss is: [1.4852213], acc is: [0.984375]
+    epoch: 1, batch_id: 200, loss is: [1.5008337], acc is: [1.]
+    epoch: 1, batch_id: 300, loss is: [1.505826], acc is: [1.]
+    epoch: 1, batch_id: 400, loss is: [1.4768786], acc is: [1.]
+    epoch: 1, batch_id: 500, loss is: [1.4950027], acc is: [0.984375]
+    epoch: 1, batch_id: 600, loss is: [1.4762383], acc is: [0.984375]
+    epoch: 1, batch_id: 700, loss is: [1.5276604], acc is: [0.96875]
+    epoch: 1, batch_id: 800, loss is: [1.4897399], acc is: [1.]
+    epoch: 1, batch_id: 900, loss is: [1.4927337], acc is: [1.]
+
+
+对模型进行验证
+~~~~~~~~~~~~~~
+
+训练完成后，需要验证模型的效果，此时，加载测试数据集，然后用训练好的模对测试集进行预测，计算损失与精度。
+
+.. code:: ipython3
+
+    import paddle
+    test_loader = paddle.io.DataLoader(test_dataset, places=paddle.CPUPlace(), batch_size=64)
+    # 加载测试数据集
+    def test(model):
+        model.eval()
+        batch_size = 64
+        for batch_id, data in enumerate(train_loader()):
+            x_data = data[0]
+            y_data = data[1]
+            predicts = model(x_data)
+            # 获取预测结果
+            loss = paddle.nn.functional.cross_entropy(predicts, y_data)
+            acc = paddle.metric.accuracy(predicts, y_data, k=2)
+            avg_loss = paddle.mean(loss)
+            avg_acc = paddle.mean(acc)
+            avg_loss.backward()
+            if batch_id % 100 == 0:
+                print("batch_id: {}, loss is: {}, acc is: {}".format(batch_id, avg_loss.numpy(), avg_acc.numpy()))
+    test(model)
+
+
+.. parsed-literal::
+
+    batch_id: 0, loss is: [1.4630548], acc is: [1.]
+    batch_id: 100, loss is: [1.4789999], acc is: [0.984375]
+    batch_id: 200, loss is: [1.4621592], acc is: [1.]
+    batch_id: 300, loss is: [1.486401], acc is: [1.]
+    batch_id: 400, loss is: [1.4767764], acc is: [1.]
+    batch_id: 500, loss is: [1.4987783], acc is: [0.984375]
+    batch_id: 600, loss is: [1.4767168], acc is: [1.]
+    batch_id: 700, loss is: [1.4876428], acc is: [0.984375]
+    batch_id: 800, loss is: [1.4924926], acc is: [0.984375]
+    batch_id: 900, loss is: [1.4799261], acc is: [1.]
+
+
+训练方式一结束
+~~~~~~~~~~~~~~
+
+以上就是训练方式一，通过这种方式，可以清楚的看到训练和测试中的每一步过程。但是，这种方式句法比较复杂。因此，我们提供了训练方式二，能够更加快速、高效的完成模型的训练与测试。
+
+3.训练方式二
+------------
+
+通过paddle提供的\ ``Model``
+构建实例，使用封装好的训练与测试接口，快速完成模型训练与测试。
+
+.. code:: ipython3
+
+    import paddle
+    from paddle.static import InputSpec
+    from paddle.metric import Accuracy
+    inputs = InputSpec([None, 784], 'float32', 'x')
+    labels = InputSpec([None, 10], 'float32', 'x')
+    model = paddle.Model(LeNet(), inputs, labels)
+    optim = paddle.optimizer.Adam(learning_rate=0.001, parameters=model.parameters())
+    
+    model.prepare(
+        optim,
+        paddle.nn.loss.CrossEntropyLoss(),
+        Accuracy(topk=(1, 2))
+        )
+
+使用model.fit来训练模型
+~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: ipython3
+
+    model.fit(train_dataset,
+            epochs=2,
+            batch_size=64,
+            save_dir='mnist_checkpoint')
+
+
+.. parsed-literal::
+
+    Epoch 1/2
+    step  10/938 - loss: 2.2252 - acc_top1: 0.2547 - acc_top2: 0.4234 - 16ms/step
+
+
+.. parsed-literal::
+
+    /Library/Python/3.7/site-packages/paddle/fluid/layers/utils.py:76: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working
+      return (isinstance(seq, collections.Sequence) and
+
+
+.. parsed-literal::
+
+    step  20/938 - loss: 1.9721 - acc_top1: 0.3664 - acc_top2: 0.5164 - 15ms/step
+    step  30/938 - loss: 1.8697 - acc_top1: 0.4464 - acc_top2: 0.5651 - 15ms/step
+    step  40/938 - loss: 1.8475 - acc_top1: 0.4859 - acc_top2: 0.5898 - 15ms/step
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+    step 938/938 - loss: 1.4621 - acc_top1: 0.8832 - acc_top2: 0.9146 - 14ms/step
+    save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/0
+    Epoch 2/2
+    step  10/938 - loss: 1.5033 - acc_top1: 0.9734 - acc_top2: 0.9906 - 15ms/step
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+    step  80/938 - loss: 1.4945 - acc_top1: 0.9719 - acc_top2: 0.9912 - 14ms/step
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+    step 900/938 - loss: 1.4620 - acc_top1: 0.9751 - acc_top2: 0.9928 - 14ms/step
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+    step 920/938 - loss: 1.4768 - acc_top1: 0.9751 - acc_top2: 0.9927 - 14ms/step
+    step 930/938 - loss: 1.4806 - acc_top1: 0.9752 - acc_top2: 0.9928 - 14ms/step
+    step 938/938 - loss: 1.4910 - acc_top1: 0.9752 - acc_top2: 0.9928 - 14ms/step
+    save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/1
+    save checkpoint at /Users/chenlong/online_repo/book/paddle2.0_docs/image_classification/mnist_checkpoint/final
+
+
+使用model.evaluate来预测模型
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. code:: ipython3
+
+    model.evaluate(test_dataset, batch_size=64)
+
+
+.. parsed-literal::
+
+    Eval begin...
+    step  10/157 - loss: 1.5014 - acc_top1: 0.9766 - acc_top2: 0.9953 - 6ms/step
+    step  20/157 - loss: 1.5239 - acc_top1: 0.9742 - acc_top2: 0.9922 - 6ms/step
+    step  30/157 - loss: 1.4926 - acc_top1: 0.9740 - acc_top2: 0.9932 - 6ms/step
+    step  40/157 - loss: 1.4612 - acc_top1: 0.9734 - acc_top2: 0.9938 - 6ms/step
+    step  50/157 - loss: 1.4612 - acc_top1: 0.9719 - acc_top2: 0.9938 - 6ms/step
+    step  60/157 - loss: 1.5114 - acc_top1: 0.9721 - acc_top2: 0.9938 - 6ms/step
+    step  70/157 - loss: 1.4793 - acc_top1: 0.9696 - acc_top2: 0.9935 - 6ms/step
+    step  80/157 - loss: 1.4736 - acc_top1: 0.9695 - acc_top2: 0.9932 - 6ms/step
+    step  90/157 - loss: 1.4892 - acc_top1: 0.9720 - acc_top2: 0.9939 - 6ms/step
+    step 100/157 - loss: 1.4623 - acc_top1: 0.9738 - acc_top2: 0.9941 - 6ms/step
+    step 110/157 - loss: 1.4612 - acc_top1: 0.9737 - acc_top2: 0.9939 - 6ms/step
+    step 120/157 - loss: 1.4612 - acc_top1: 0.9746 - acc_top2: 0.9939 - 6ms/step
+    step 130/157 - loss: 1.4703 - acc_top1: 0.9757 - acc_top2: 0.9942 - 6ms/step
+    step 140/157 - loss: 1.4612 - acc_top1: 0.9771 - acc_top2: 0.9946 - 6ms/step
+    step 150/157 - loss: 1.4748 - acc_top1: 0.9782 - acc_top2: 0.9950 - 6ms/step
+    step 157/157 - loss: 1.4612 - acc_top1: 0.9770 - acc_top2: 0.9949 - 6ms/step
+    Eval samples: 10000
+
+
+
+
+.. parsed-literal::
+
+    {'loss': [1.4611504], 'acc_top1': 0.977, 'acc_top2': 0.9949}
+
+
+
+训练方式二结束
+~~~~~~~~~~~~~~
+
+以上就是训练方式二，可以快速、高效的完成网络模型训练与预测。
+
+总结
+----
+
+以上就是用LeNet对手写数字数据及MNIST进行分类。本示例提供了两种训练模型的方式，一种可以快速完成模型的组建与预测，非常适合新手用户上手。另一种则需要多个步骤来完成模型的训练，适合进阶用户使用。
diff --git a/doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification_files/mnist_lenet_classification_6_1.png b/doc/paddle/tutorial/cv_case/mnist_lenet_classification/mnist_lenet_classification_files/mnist_lenet_classification_6_1.png
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index 0000000000000000000000000000000000000000..91327f435ad51a582cc577c32fcaef3126e383b6
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diff --git a/doc/paddle/tutorial/index_cn.rst b/doc/paddle/tutorial/index_cn.rst
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@@ -0,0 +1,19 @@
+########
+应用实践
+########
+
+如果您已经初步了解了PaddlePaddle，期望可以针对实际问题建模、搭建自己网络，本模块提供了一些 Paddle 的具体典型案例供您参考：
+
+本章文档将指导您如何使用PaddlePaddle完成基础的深度学习任务
+
+本章文档涉及大量了深度学习基础知识，也介绍了如何使用PaddlePaddle实现这些内容，请参阅以下说明了解如何使用：
+
+
+**内容简介**
+
+    - `计算机视觉 <./cv_case/index_cn.html>`_ ：介绍使用 Paddle 解决计算机视觉领域的案例
+
+..  toctree::
+    :hidden:
+
+    cv_case/index_cn.rst