diff --git a/01.03 机器学习常用的包.ipynb b/01.03 机器学习常用的包.ipynb
index a6cfe30..0c37177 100644
--- a/01.03 机器学习常用的包.ipynb
+++ b/01.03 机器学习常用的包.ipynb
@@ -953,141 +953,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 广播机制"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "正常的加法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([[ 0, 0, 0],\n",
-    "              [10,10,10],\n",
-    "              [20,20,20],\n",
-    "              [30,30,30]])\n",
-    "b = np.array([[ 0, 1, 2],\n",
-    "              [ 0, 1, 2],\n",
-    "              [ 0, 1, 2],\n",
-    "              [ 0, 1, 2]])\n",
-    "a + b\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "将 `b` 的值变成二维的 `[0,1,2]` 之后的加法："
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "b = np.array([[0,1,2],[0,1,2]])\n",
-    "print(b.shape)\n",
-    "a + b\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "两个 `ndarray` 执行的是对应元素的的运算，广播机制的功能是为了方便不同形状的`ndarray`（`NumPy` 库的核心数据结构）进行数学运算。\n",
-    "  \n",
-    " 当操作两个数组时，`NumPy` 会逐个比较它们的`shape`（构成的元组`tuple`），只有在下述情况下，两个数组才能够进行数组与数组的运算。\n",
-    "\n",
-    "+ 维度相等\n",
-    "+ `shape`（其中相对应的一个地方为 `1`）"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 将 `b` 的值变成一维的 `[0,1,2]` 之后的加法：\n",
-    "b = np.array([0,1,2])\n",
-    "\n",
-    "a + b\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "结果一样，虽然两个数组的维数不一样，但是 **Numpy** 检测到 `b` 的维度与 `a` 的维度匹配，所以将 `b` 扩展为之前的形式，得到相同的形状。\n",
-    "\n",
-    "对于更高维度，这样的扩展依然有效。 \n",
-    "\n",
-    "如果我们再将 `a` 变成一个列向量呢？"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a = np.array([0,10,20,30])\n",
-    "a.shape = 4,1\n",
-    "a\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "a + b\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# 此时a，b的维度分别为\n",
-    "a.shape, b.shape\n"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "可以看到，虽然两者的维度并不相同，但是 `Numpy` 还是根据两者的维度，自动将它们进行扩展然后进行计算。\n",
-    "\n",
-    "匹配会从最后一维开始进行，直到某一个的维度全部匹配为止，因此对于以下情况，`Numpy` 都会进行相应的匹配：\n",
-    "\n",
-    "A|B|Result\n",
-    "---|---|---\n",
-    "3d array: 256 x 256 x 3 | 1d array: 3 | 3d array: 256 x 256 x 3\n",
-    "4d array: 8 x 1 x 6 x 1 | 3d array: 7 x 1 x 5 | 3d array: 8 x 7 x 6 x 5\n",
-    "3d array: 5 x 4 x 3 | 1d array: 1 | 3d array: 5 x 4 x 3\n",
-    "3d array: 15 x 4 x 13 | 1d array: 15 x 1 x 13 | 3d array: 15 x 4 x 13\n",
-    "2d array: 4 x 1 | 1d array: 3 | 2d array: 4 x 3\n",
-    "\n",
-    "匹配成功后，`Numpy` 会进行运算得到相应的结果。\n",
-    "\n",
-    "当然，如果相应的维度不匹配，那么 `Numpy` 会报错：`ValueError`。"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
     "### `IO` 操作"
    ]
   },
diff --git a/02.03 回归分析(学生版).ipynb b/02.03 回归分析(学生版).ipynb
index 08c33dd..705adae 100644
--- a/02.03 回归分析(学生版).ipynb
+++ b/02.03 回归分析(学生版).ipynb
@@ -59,6 +59,8 @@
     "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
     "%matplotlib inline\n",
+    "!mkdir -p ~/.keras/datasets\n",
+    "!cp ./mnist.npz ~/.keras/datasets/mnist.npz\n",
     "\n",
     "x = np.array([1970, 1975, 1980, 1985, 1990, 1995, 2000, 2005])\n",
     "y = np.array([325.68, 331.15, 338.69, 345.90, 354.19, 360.88, 369.48, 379.67])\n",
diff --git a/iris.csv b/iris.csv
new file mode 100644
index 0000000..20bd6ee
--- /dev/null
+++ b/iris.csv
@@ -0,0 +1,151 @@
+sepal_length,sepal_width,petal_length,petal_width,species
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+5.0,3.4,1.6,0.4,setosa
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+4.9,3.1,1.5,0.2,setosa
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+5.0,3.5,1.3,0.3,setosa
+4.5,2.3,1.3,0.3,setosa
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+5.0,3.5,1.6,0.6,setosa
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+4.8,3.0,1.4,0.3,setosa
+5.1,3.8,1.6,0.2,setosa
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+5.3,3.7,1.5,0.2,setosa
+5.0,3.3,1.4,0.2,setosa
+7.0,3.2,4.7,1.4,versicolor
+6.4,3.2,4.5,1.5,versicolor
+6.9,3.1,4.9,1.5,versicolor
+5.5,2.3,4.0,1.3,versicolor
+6.5,2.8,4.6,1.5,versicolor
+5.7,2.8,4.5,1.3,versicolor
+6.3,3.3,4.7,1.6,versicolor
+4.9,2.4,3.3,1.0,versicolor
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+5.0,2.0,3.5,1.0,versicolor
+5.9,3.0,4.2,1.5,versicolor
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