resolve ipynb conflicts

fit_a_line/README.en.ipynb

@@ -7,7 +7,7 @@
         "# Linear Regression\n",
         "Let us begin the tutorial with a classical problem called Linear Regression \\[[1](#References)\\]. In this chapter, we will train a model from a realistic dataset to predict home prices. Some important concepts in Machine Learning will be covered through this example.\n",
         "\n",
-        "The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html).\n",
+        "The source code for this tutorial lives on [book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line). For instructions on getting started with PaddlePaddle, see [PaddlePaddle installation guide](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_en.rst).\n",
         "\n",
         "## Problem Setup\n",
         "Suppose we have a dataset of $n$ real estate properties. These real estate properties will be referred to as *homes* in this chapter for clarity.\n",
@@ -384,7 +384,7 @@
         "4. Bishop C M. Pattern recognition[J]. Machine Learning, 2006, 128.\n",
         "\n",
         "\u003cbr/\u003e\n",
-        "\u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"\u003e\u003cimg alt=\"Common Creative License\" style=\"border-width:0\" src=\"https://i.creativecommons.org/l/by-nc-sa/4.0/88x31.png\" /\u003e\u003c/a\u003e This tutorial was created and published with [Creative Common License 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/).\n"
+        "This tutorial is contributed by \u003ca xmlns:cc=\"http://creativecommons.org/ns#\" href=\"http://book.paddlepaddle.org\" property=\"cc:attributionName\" rel=\"cc:attributionURL\"\u003ePaddlePaddle\u003c/a\u003e, and licensed under a \u003ca rel=\"license\" href=\"http://creativecommons.org/licenses/by-nc-sa/4.0/\"\u003eCreative Commons Attribution-NonCommercial-ShareAlike 4.0 International License\u003c/a\u003e.\n"
       ]
     }
   ],

fit_a_line/README.ipynb

@@ -7,7 +7,7 @@
         "# 线性回归\n",
         "让我们从经典的线性回归（Linear Regression \\[[1](#参考文献)\\]）模型开始这份教程。在这一章里，你将使用真实的数据集建立起一个房价预测模型，并且了解到机器学习中的若干重要概念。\n",
         "\n",
-        "本教程源代码目录在[book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line)， 初次使用请参考PaddlePaddle[安装教程](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html)。\n",
+        "本教程源代码目录在[book/fit_a_line](https://github.com/PaddlePaddle/book/tree/develop/fit_a_line)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。\n",
         "\n",
         "## 背景介绍\n",
         "给定一个大小为$n$的数据集  ${\\{y_{i}, x_{i1}, ..., x_{id}\\}}_{i=1}^{n}$，其中$x_{i1}, \\ldots, x_{id}$是第$i$个样本$d$个属性上的取值，$y_i$是该样本待预测的目标。线性回归模型假设目标$y_i$可以被属性间的线性组合描述，即\n",
@@ -21,8 +21,8 @@
         "## 效果展示\n",
         "我们使用从[UCI Housing Data Set](https://archive.ics.uci.edu/ml/datasets/Housing)获得的波士顿房价数据集进行模型的训练和预测。下面的散点图展示了使用模型对部分房屋价格进行的预测。其中，每个点的横坐标表示同一类房屋真实价格的中位数，纵坐标表示线性回归模型根据特征预测的结果，当二者值完全相等的时候就会落在虚线上。所以模型预测得越准确，则点离虚线越近。\n",
         "\u003cp align=\"center\"\u003e\n",
-        "\t\u003cimg src = \"image/predictions.png\" width=400\u003e\u003cbr/\u003e\n",
-        "\t图1. 预测值 V.S. 真实值\n",
+        "    \u003cimg src = \"image/predictions.png\" width=400\u003e\u003cbr/\u003e\n",
+        "    图1. 预测值 V.S. 真实值\n",
         "\u003c/p\u003e\n",
         "\n",
         "## 模型概览\n",
@@ -124,8 +124,8 @@
         "- 很多的机器学习技巧/模型（例如L1，L2正则项，向量空间模型-Vector Space Model）都基于这样的假设：所有的属性取值都差不多是以0为均值且取值范围相近的。\n",
         "\n",
         "\u003cp align=\"center\"\u003e\n",
-        "\t\u003cimg src = \"image/ranges.png\" width=550\u003e\u003cbr/\u003e\n",
-        "\t图2. 各维属性的取值范围\n",
+        "    \u003cimg src = \"image/ranges.png\" width=550\u003e\u003cbr/\u003e\n",
+        "    图2. 各维属性的取值范围\n",
         "\u003c/p\u003e\n",
         "\n",
         "#### 整理训练集与测试集\n",

recommender_system/README.ipynb

@@ -6,7 +6,7 @@
       "source": [
         "# 个性化推荐\n",
         "\n",
-        "本教程源代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/recommender_system)， 初次使用请参考PaddlePaddle[安装教程](http://www.paddlepaddle.org/doc_cn/build_and_install/index.html)。\n",
+        "本教程源代码目录在[book/recommender_system](https://github.com/PaddlePaddle/book/tree/develop/recommender_system)， 初次使用请参考PaddlePaddle[安装教程](https://github.com/PaddlePaddle/Paddle/blob/develop/doc/getstarted/build_and_install/docker_install_cn.rst)。\n",
         "\n",
         "## 背景介绍\n",
         "\n",