http://git-wip-us.apache.org/repos/asf/incubator-singa/blob/6ef4bbee/doc/notebook/regression.ipynb
----------------------------------------------------------------------
diff --git a/doc/notebook/regression.ipynb b/doc/notebook/regression.ipynb
index 294b692..4e81a20 100755
--- a/doc/notebook/regression.ipynb
+++ b/doc/notebook/regression.ipynb
@@ -6,12 +6,12 @@
    "source": [
     "# Train a linear regression model\n",
     "\n",
-    "In this notebook, we are going to use the tensor module from PySINGA to 
train a linear regression model. The training would be conducted using numpy. 
We use this example to illustrate the usage of tensor of PySINGA. Please 
install 
[PySINGA](http://singa.apache.org/en/docs/installation.html#install-pysinga) 
before executing the following code. "
+    "In this notebook, we are going to use the tensor module from PySINGA to 
train a linear regression model. We use this example to illustrate the usage of 
tensor of PySINGA. Please refer the [documentation 
page](http://singa.apache.org/en/docs/tensor.html) to for more tensor functions 
provided by PySINGA. "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 1,
    "metadata": {
     "collapsed": true
    },
@@ -31,7 +31,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 2,
    "metadata": {
     "collapsed": false
    },
@@ -52,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 3,
    "metadata": {
     "collapsed": false
    },
@@ -60,18 +60,18 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7ff77e6437d0>"
+       "<matplotlib.legend.Legend at 0x7fce59cef510>"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
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       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff77e6434d0>"
+       "<matplotlib.figure.Figure at 0x7fce59cef550>"
       ]
      },
      "metadata": {},
@@ -83,7 +83,7 @@
     "f = lambda x: a * x + b\n",
     "gx = np.linspace(0.,1,100)\n",
     "gy = [f(x) for x in gx]\n",
-    "plt.plot(gx, gy, label='y=f(x)')\n",
+    "plt.plot(gx, gy,  label='y=f(x)')\n",
     "plt.xlabel('x')\n",
     "plt.ylabel('y')\n",
     "plt.legend(loc='best')\n"
@@ -101,7 +101,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 30,
+   "execution_count": 4,
    "metadata": {
     "collapsed": false
    },
@@ -109,18 +109,18 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7ff777debb50>]"
+       "[<matplotlib.lines.Line2D at 0x7fce43e79390>]"
       ]
      },
-     "execution_count": 30,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 
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/5jG0NC3U45pRqK9wBfK8c\nHP4YOLvRn6OVliqO+W8B/698zL9Jqe7CykZ/jlZZgN5ySJj17/NSnUOdSEqSJGXyaQhJkpTJsCBJ\nkjIZFiRJUibDgiRJymRYkCRJmQwLkiQpk2FBkiRlMixIkqRMhgVJkpTJsCBJkjIZFiRJUqb/DzjG\nuFavUES/AAAAAElFTkSuQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x7ff77c270290>"
+       "<matplotlib.figure.Figure at 0x7fce59e405d0>"
       ]
      },
      "metadata": {},
@@ -149,7 +149,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 31,
+   "execution_count": 6,
+   "metadata": {
+    "collapsed": false
+   },
+   "outputs": [],
+   "source": [
+    "def plot(idx, x, y):\n",
+    "    global gx, gy, axes\n",
+    "    # print the ground truth line\n",
+    "    axes[idx/5, idx%5].plot(gx, gy, label='y=f(x)')     \n",
+    "    # print the learned line\n",
+    "    axes[idx/5, idx%5].plot(x, y, label='y=kx+b')\n",
+    "    axes[idx/5, idx%5].legend(loc='best')\n",
+    "\n",
+    "# set hyper-parameters\n",
+    "max_iter = 15\n",
+    "alpha = 0.1\n",
+    "\n",
+    "# init parameters\n",
+    "k, b = 2.,0."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "SINGA tensor module supports basic linear algebra operations, like `+ - * 
/`, and advanced functions including axpy, gemm, gemv, and random function 
(e.g., Gaussian and Uniform).\n",
+    "\n",
+    "SINGA Tensor instances could be created via **tensor.Tensor()** by 
specifying the shape, and optionally the device and data type. Note that every 
Tensor instance should be initialized (e.g., via **set_value()** or random 
functions) before reading data from it. You can also create Tensor instances 
from numpy arrays,\n",
+    "\n",
+    "* numpy array could be converted into SINGA tensor via 
**tensor.from_numpy(np_ary)** \n",
+    "* SINGA tensor could be converted into numpy array via 
**tensor.to_numpy()**; Note that the tensor should be on the host device. 
tensor instances could be transferred from other devices to host device via 
**to_host()**\n",
+    "\n",
+    "Users cannot read a single cell of the Tensor instance. To read a single 
cell, users need to convert the Tesnor into a numpy array.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
    "metadata": {
     "collapsed": false
    },
@@ -158,28 +196,28 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "9.18032938639\n",
-      "6.99725952148\n",
-      "5.33929697673\n",
-      "4.08013102214\n",
-      "3.12383524577\n",
-      "2.39755630493\n",
-      "1.84596570333\n",
-      "1.42704442342\n",
-      "1.10888010661\n",
-      "0.867236455282\n",
-      "0.6837073644\n",
-      "0.544314193726\n",
-      "0.438440195719\n",
-      "0.358023166656\n",
-      "0.29693962733\n"
+      "8.4457921346\n",
+      "6.52662099202\n",
+      "5.04807383219\n",
+      "3.90897369385\n",
+      "3.03137512207\n",
+      "2.35523325602\n",
+      "1.83428827922\n",
+      "1.43290456136\n",
+      "1.12362861633\n",
+      "0.885310490926\n",
+      "0.701658376058\n",
+      "0.560119374593\n",
+      "0.451024500529\n",
+      "0.366924413045\n",
+      "0.3020805041\n"
      ]
     },
     {
      "data": {
-      "image/png": 
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