diff --git a/2_pytorch/1_NN/linear-regression-gradient-descend.ipynb b/2_pytorch/1_NN/linear-regression-gradient-descend.ipynb
index 134ce60..c8f675e 100644
--- a/2_pytorch/1_NN/linear-regression-gradient-descend.ipynb
+++ b/2_pytorch/1_NN/linear-regression-gradient-descend.ipynb
@@ -128,7 +128,7 @@
     {
      "data": {
       "text/plain": [
-       "<torch._C.Generator at 0x7f164408bab0>"
+       "<torch._C.Generator at 0x7ff9fe679ab0>"
       ]
      },
      "execution_count": 1,
@@ -146,7 +146,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,16 +162,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f15f17862e8>]"
+       "[<matplotlib.lines.Line2D at 0x7ff9ca7aa240>]"
       ]
      },
-     "execution_count": 3,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -198,7 +198,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -213,7 +213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -227,7 +227,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,22 +243,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f15f16f6828>"
+       "<matplotlib.legend.Legend at 0x7ff9ca7696a0>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -295,7 +295,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -308,14 +308,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor(153.3520, grad_fn=<MeanBackward1>)\n"
+      "tensor(94.9309, grad_fn=<MeanBackward1>)\n"
      ]
     }
    ],
@@ -338,7 +338,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -348,15 +348,15 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([161.0043])\n",
-      "tensor([22.8730])\n"
+      "tensor([-126.6150])\n",
+      "tensor([-18.3376])\n"
      ]
     }
    ],
@@ -392,7 +392,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f15ee64be80>"
+       "<matplotlib.legend.Legend at 0x7ff9ca73dac8>"
       ]
      },
      "execution_count": 17,
@@ -401,7 +401,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -435,16 +435,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch: 0, loss: 0.2959636151790619\n",
-      "epoch: 1, loss: 0.2953118681907654\n",
-      "epoch: 2, loss: 0.2946634888648987\n",
-      "epoch: 3, loss: 0.29401835799217224\n",
-      "epoch: 4, loss: 0.2933765947818756\n",
-      "epoch: 5, loss: 0.292738139629364\n",
-      "epoch: 6, loss: 0.29210299253463745\n",
-      "epoch: 7, loss: 0.29147112369537354\n",
-      "epoch: 8, loss: 0.2908424139022827\n",
-      "epoch: 9, loss: 0.29021692276000977\n"
+      "epoch: 0, loss: 1.9595526456832886\n",
+      "epoch: 1, loss: 0.23876741528511047\n",
+      "epoch: 2, loss: 0.20673297345638275\n",
+      "epoch: 3, loss: 0.2059527039527893\n",
+      "epoch: 4, loss: 0.20575186610221863\n",
+      "epoch: 5, loss: 0.2055628001689911\n",
+      "epoch: 6, loss: 0.20537473261356354\n",
+      "epoch: 7, loss: 0.20518775284290314\n",
+      "epoch: 8, loss: 0.20500165224075317\n",
+      "epoch: 9, loss: 0.2048165202140808\n"
      ]
     },
     {
@@ -478,7 +478,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f15ee6181d0>"
+       "<matplotlib.legend.Legend at 0x7ff9c86fd8d0>"
       ]
      },
      "execution_count": 19,
@@ -487,7 +487,7 @@
     },
     {
      "data": {
-      "image/png": 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k7emS1kr6kaRzhvj3cyV1SOro6ekpu7FmqTvawHcVx4Bc2dPKlTjcJZ0MPAN8PSJ+P+DtNcD4iJgM3Ac8O9gxImJJRLRFRFtzc/OxttksHaUGvqs4BuTKnlauRPPcJTUBK4B/johFCfbvAtoi4ndD7eN57lZ3Up67vnRpYYx9y5ZCj33hQq+HakRJ57mXHHOXJOARYMNQwS7pj4DfRkRIupDCXwTby2yzWbalPPDtyp5WjiTlBy4CvgD8SlJncds3gRaAiHgQ+Bxwi6SDwF7g+khr6atZtQx1wdQD35ZBSWbLvAyoxD73A/dXqlFmmbRw4ZH1YsAD35ZZXqFqlpRvaWV1xFUhzcrhgW+rE+65m5nlkMPdzCyHHO5mZjnkcDczyyGHu+WfC6FbA/JsGcs338vUGpR77pZvLoRuDcrhbvnmQujWoBzulm8uhG4NyuFu+eZC6NagHO6Wb64HYw3Ks2Us/1wPxhqQe+5mZjnkcLfUeY2RWeV5WMZS5TVGZtXhnrulymuMzKrD4W6p8hojs+pwuFuqvMbIrDpKhrukcZJWSVov6Q1Jtw+yjyT9vaRNktZJmlqd5lreeI2RWXUk6bkfBL4REROAacBtkiYM2OdPgY8XH3OBByraSsstrzEyq46S4R4RWyNiTfH5B8AG4MwBu10NPB4F/wKcJumMirfWcqmdpXTRSi8j6KKVdjwX0my4yhpzl9QKnAe8MuCtM4F3+73u5g+/AJA0V1KHpI6enp7yWmr51DcXcvNmiPhwLqQnu5sNS+Jwl3Qy8Azw9Yj4/bGcLCKWRERbRLQ1NzcfyyEsbzwX0qwqEoW7pCYKwb40In44yC7vAeP6vR5b3GZ2dJ4LaVYVSWbLCHgE2BARi4bYbTlwY3HWzDRgZ0RsrWA7rd4NVWPAcyHNqiJJ+YGLgC8Av5LUWdz2TaAFICIeBFYClwObgD3An1W+qVa3jlZjYOHCI98Dz4U0q4CS4R4RLwMqsU8At1WqUZYzRxtX7+r6cJ8tWwo99oULPRfSbJhUyOXaa2tri46OjlTObTU2YkRhJsxAEvT21r49ZnVM0uqIaCu1n8sPWPV5XN2s5hzuVn2uMWBWcw53qz7XGDCrOd+sw2rD9zE1qyn33M3McsjhbmaWQw53M7MccrhX0FAr7M3Mas3hXiGuXGvgL3jLDod7hbhyrfkL3rLE4V4hrlx7dI3Qo/UXvGWJw71CvMJ+aI3So/UXvGWJw71CvMJ+aI3So/UXvGWJw71CvMJ+aI3So/UXvGWJw72C2tsL5cl7ews/HewFjdKj9Re8ZYnDPSeyfMGykXq0/oK3rHC450DWL1i6R2tWew73HEh0wTLlrr17tGa1VTLcJX1P0jZJrw/x/gxJOyV1Fh93V76ZdjQlL1hmvWtvZhWXpOf+KDC7xD4vRcSU4mPB8Jtl5Sh5wbJR5iKa2WElwz0iXgR21KAtdoxKXrBslLmIZnZYpcbcp0taK+lHks6p0DEtoZIXLBtlLqKZHVaJcF8DjI+IycB9wLND7ShprqQOSR09PT0VOLX1aWcpXbTSywi6aKWdfuPpjTQX0cyACoR7RPw+InYVn68EmiSNGWLfJRHRFhFtzc3Nwz219Sl1wdRzEc0ajiKi9E5SK7AiIiYO8t4fAb+NiJB0IbCMQk/+qAdua2uLjo6OY2q0DdDaWgj0gcaPL8w7NLPckLQ6ItpK7XdcggM9BcwAxkjqBv4CaAKIiAeBzwG3SDoI7AWuLxXsVmG+YGpmA5QM94i4ocT79wP3V6xFVr6WlsF77r5gatawvEI1D3zB1MwGcLjngS+YmtkAJYdlrE60tzvMzeww99zLlOXSumZmfdxzL0PfdPK+Mi1908nBnWYzyxb33Mvg+ltmVi8c7mXwdHIzqxcO9zK4/paZ1Yu6Cve0L2Z6OrmZ1Yu6Cfcs3EzI08nNrF4kKhxWDeUWDnNtLDOz5IXD6qbn7ouZZmbJ1U24+2KmmVlydRPuvphpZpZc3YS7L2aamSVXN+EOhSDv6oLe3sLPzAV72nM1zcyKXFumUlx4xswypK567pnmwjNmliEO90rxXE0zyxCHe6V4rqaZZUjJcJf0PUnbJL0+xPuS9PeSNklaJ2lq5ZuZIUNdNPVcTTPLkCQXVB8F7gceH+L9PwU+Xnz8V+CB4s/8SXLRdP78wlBMS0sh2H0x1cxSkKi2jKRWYEVETBzkvYeAn0bEU8XXbwEzImLr0Y5Zbm2ZTHCBGzNLWS1ry5wJvNvvdXdxW/74oqmZ1YmaXlCVNFdSh6SOnp6eWp66MnzR1MzqRCXC/T1gXL/XY4vb/kBELImItohoa25ursCpa8wXTc2sTlQi3JcDNxZnzUwDdpYab69bLnBjZnWi5GwZSU8BM4AxkrqBvwCaACLiQWAlcDmwCdgD/Fm1GpsJ7e0OczPLvJLhHhE3lHg/gNsq1iIzMxs2r1A1M8shh7uZWQ453M3McsjhbmaWQw53M7MccribmeWQw93MLIcc7mZmOeRwNzPLIYe7mVkOOdzNzHLI4W5mlkP1Fe5D3ZzazMyOkOQG2dmQ5ObUZmYG1FPPff78D4O9z549he1mZnaE+gl335zazCyx+gl335zazCyx+gl335zazCyx+gl335zazCyx+pktA745tZlZQol67pJmS3pL0iZJdw7y/hxJPZI6i4+bK99UMzNLqmTPXdJI4B+AWUA38Jqk5RGxfsCuT0fEvCq00czMypSk534hsCki3omI/cA/AldXt1lmZjYcScL9TODdfq+7i9sGulbSOknLJI2rSOvMzOyYVGq2zD8BrRExCXgOeGywnSTNldQhqaOnp6dCpzYzs4GSzJZ5D+jfEx9b3HZYRGzv9/Jh4DuDHSgilgBLAIoXYDcnOP8Y4HcJ9ms0/lyG5s9mcP5chlZPn834JDslCffXgI9LOotCqF8P/Pf+O0g6IyK2Fl9eBWwoddCIaE7SQEkdEdGWZN9G4s9laP5sBufPZWh5/GxKhntEHJQ0D/hnYCTwvYh4Q9ICoCMilgNfk3QVcBDYAcypYpvNzKyERIuYImIlsHLAtrv7Pb8LuKuyTTMzs2NVD+UHlqTdgIzy5zI0fzaD8+cytNx9NoqItNtgZmYVVg89dzMzK1Mmw13SOEmrJK2X9Iak29NuU5ZIGinpXyWtSLstWSLptOIiujclbZA0Pe02ZYWk/1H8v/S6pKckjUq7TWmR9D1J2yS93m/bf5b0nKS3iz//U5ptrIRMhjuFWTffiIgJwDTgNkkTUm5TltxOgummDei7wP+LiP8CTMafEQCSzgS+BrRFxEQKs96uT7dVqXoUmD1g253A8xHxceD54uu6lslwj4itEbGm+PwDCv9JByt50HAkjQWuoLBYzIoknQp8CngEICL2R8S/p9uqTDkOOEHSccCJwG9Sbk9qIuJFClO2+7uaD1fWPwZ8tqaNqoJMhnt/klqB84BX0m1JZvwd8OdAb9oNyZizgB7gfxeHrB6WdFLajcqCiHgP+FtgC7AV2BkRP063VZlzer+FmP8GnJ5mYyoh0+Eu6WTgGeDrEfH7tNuTNklXAtsiYnXabcmg44CpwAMRcR6wmxz8aV0JxfHjqyl8AX4UOEnS59NtVXZFYQph3U8jzGy4S2qiEOxLI+KHabcnIy4CrpLURaH08sWSnky3SZnRDXRHRN9feMsohL3BTODXEdETEQeAHwJ/knKbsua3ks6AQjkVYFvK7Rm2TIa7JFEYO90QEYvSbk9WRMRdETE2IlopXBB7ISLcAwMi4t+AdyWdXdx0CTDwhjKNagswTdKJxf9bl+CLzQMtB24qPr8J+L8ptqUiMhnuFHqoX6DQM+27dd/laTfKMu+rwFJJ64ApwF+l3J5MKP41swxYA/yKwv/73K3ITErSU8AvgbMldUv6IvDXwCxJb1P4S+ev02xjJXiFqplZDmW1525mZsPgcDczyyGHu5lZDjnczcxyyOFuZpZDDnczsxxyuJuZ5ZDD3cwsh/4/cs44BOTE4+EAAAAASUVORK5CYII=\n",
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wl/zlDHfgUuATwE/NrDOz7fNAI4C73wt8FLjZzI4Bh4CPe1yrgIiUSrYLpur4lgTKZ7TMs4Dl2Odu4O5iFSWSSGvWRH3sI8eyq+NbEkqfUBXJl9YxlQqST7eMiAxRx7dUCLXcRUQCpHAXEQmQwl1EJEAKdxGRACncJXyaCF2qkEbLSNiG5oMZGps+NB8MaNSLBE0tdwmb1jKVKqVwl7BpInSpUgp3CZsmQpcqpXCXsGkidKlSCncJm+aDkSql0TISPs0HI1VILXcRkQAp3EVEAqRwFxEJkMJdRCRACncRkQAp3EVEAqRwl9hp0kaR4tM4d4mVJm0UKQ213CVWra1wdX87r5FmgBpeI83V/e2atFFkgtRyl1hd2tNOGy1MJmq6p+nh32ihpQdATXeR8VLLXWL15drW4WAfMpl+vlyrprvIRCjcJVbnDYw9r3q27SKSn5zhbmYzzGyzmXWZ2ctmdssY+5iZfc3MdprZi2Y2rzTlSsXKMiTGUmPPq55tu4jkJ58+92PAX7n7VjM7A9hiZt9x964R+/wJ8O7M7Q+BezL3IicfErNmzYmvgeZbFymCnC13d9/t7lszj98CtgPnjdrtauAhj/wYOMvMphW9WqlMJ1vHVPOti5REQaNlzCwNXAw8N+ql84DXRzzvzWzbPerrW4AWgEYtc1Y9cq1jqvnWRYou7wuqZnY68BjwOXf/7XgO5u5t7t7k7k0NDQ3jeQupRFrHVKTs8gp3M6sjCvZ2d//WGLu8AcwY8Xx6ZpuI1jEViUE+o2UMeADY7u5rs+z2BHB9ZtTMQmC/u+/Osq9UG/Wri5RdPn3ulwKfAH5qZp2ZbZ8HGgHc/V7gSeBDwE6gH/iz4pcqFU396iJllTPc3f1ZwHLs48Cni1WUiIhMjD6hKiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhLiISIIW7iEiAFO4iIgFSuIuIBEjhXkRZ1oBOhkQXFw6dZkmKgpbZk+xOtgZ07DPdJrq4cOg0S5JYNFtv+TU1NXlHR0csxy6FdDr6YR4tlYLu7nJXM0qiiwuHTrOUg5ltcfemXPupW6ZIcq0BHasEFFcN3RUJOM0iwxTuRZLoNaBjLm6ou6KnB9yPd1eEFvCJ/j8gVUfhXiSxrwF9sqZxzMW1th7vhx7S3x9tD0ns/wdERnL3WG7z58/30DzyiHsq5W4W3T/ySBkPXF/vHjWMo1t9/YkFxFZcdMiRpQ3dzMpWQtnEeJqlSgAdnkfG6oJqCBJ+JS/h5YlUFF1QrSYJv5Kn7gqR8lO4B+DAO8e+Ypdte7k1N0NbW9RSN4vu29o09luklBTuAfg8azjIiU3jg9TzeZLTNG5ujrpgBgejewW7SGkp3ANw975mbqKNblIMYnST4ibauHufElSkWmn6gQA0NsKGnmY2cGKYp5LRKyMiMcjZcjezr5vZHjN7Kcvri81sv5l1Zm5fKH6ZcjK6YCkio+XTLbMeWJZjn++7+9zM7Y6JlyWF0AVLERktZ7eMuz9jZunSlyIT0dysMBeR44p1QXWRmW0zs/8xswuz7WRmLWbWYWYdfX19RTq0iIiMVoxw3wqk3H0OcBfweLYd3b3N3ZvcvamhoaEIhxYRkbFMONzd/bfufiDz+EmgzsymTrgyEREZtwmHu5mdY2aWeXxJ5j33TvR9RURk/HJeUDWzDcBiYKqZ9QJ/D9QBuPu9wEeBm83sGHAI+LjHNRuZiIgA+Y2WWZ7j9buBu4tWkYiITJimHxARCZDCXUQkQAp3EZEAKdyL6WTrmIqIlJFmhSyW9nZoaTm+EnRPT/QcNC+AiJSdWu4Fyto4b209HuxD+vuj7SIiZaaWewFO2jhP+DqmIlJd1HIvwEkb541ZVsbItl1EpIQU7gXYtQuW085rpBmghtdIs5z2qHGuFTNEJEHULVOAVe9s5869LUwmar6n6eHfaGHqOzl+0bS1Nfot0NgYBbsupopIDCqq5R73SMMv0Toc7EMm08+XyFw0bW6G7m4YHIzuFewiEpOKabknYaTh6fvGvjiabbuISFwqpuWeiJGGumgqIhWiYsI9ESMNddFURCpExYR7IhrNzc3Q1gapFJhF921t6lsXkcSpmHBPTKNZF01FpAJUTLir0Swikr+KGS0DUZArzEVEcquYlruIiORP4S4iEiCFu4hIgBTuIiIBUriLiARI4S4iEiCFu4hIgHKGu5l93cz2mNlLWV43M/uame00sxfNbF7xyxQRkULk03JfDyw7yet/Arw7c2sB7pl4WSIiMhE5w93dnwH2nWSXq4GHPPJj4Cwzm1asAkVEpHDF6HM/D3h9xPPezDYREYlJWS+omlmLmXWYWUdfX1/hbxD3OnsiIhWiGOH+BjBjxPPpmW2/w93b3L3J3ZsaGhoKO8rQOns9PeB+fJ09BbyIyO8oRrg/AVyfGTWzENjv7ruL8L4nSsQ6eyIilSHnlL9mtgFYDEw1s17g74E6AHe/F3gS+BCwE+gH/qwklSZinT0RkcqQM9zdfXmO1x34dNEqyqaxMeqKGWu7iIicoHI+oZqYdfZERJKvcsJd6+yJiOStopbZ0zp7IiL5qZyWu4iI5E3hLiISIIW7iEiAFO4iIgFSuIuIBMiizyDFcGCzPmCMTyX9jqnAr0pcTiXSeclO52ZsOi/ZVdK5Sbl7zsm5Ygv3fJlZh7s3xV1H0ui8ZKdzMzadl+xCPDfqlhERCZDCXUQkQJUQ7m1xF5BQOi/Z6dyMTeclu+DOTeL73EVEpHCV0HIXEZECJTLczWyGmW02sy4ze9nMbom7piQxs1oz+4mZbYq7liQxs7PMbKOZ/czMtpvZorhrSgoz+4vMz9JLZrbBzCbFXVNczOzrZrbHzF4ase2dZvYdM3slc/97cdZYDIkMd+AY8FfuPhNYCHzazGbGXFOS3AJsj7uIBPoq8L/u/gfAHHSOADCz84DPAk3uPguoBT4eb1WxWg8sG7XtNuApd3838FTmeUVLZLi7+25335p5/BbRD+l58VaVDGY2HbgSuD/uWpLEzM4E3g88AODuR9z9N/FWlSinAKeZ2SlAPfBmzPXExt2fAfaN2nw18GDm8YPANWUtqgQSGe4jmVkauBh4Lt5KEuOfgb8BBuMuJGHOB/qAf890Wd1vZpPjLioJ3P0N4J+AXcBuokXsvx1vVYlztrvvzjz+BXB2nMUUQ6LD3cxOBx4DPufuv427nriZ2VXAHnffEnctCXQKMA+4x90vBg4SwJ/WxZDpP76a6BfgucBkM/vTeKtKrsy60BU/jDCx4W5mdUTB3u7u34q7noS4FPiImXUD/wFcZmaPxFtSYvQCve4+9BfeRqKwF1gCvObufe5+FPgW8Ecx15Q0vzSzaQCZ+z0x1zNhiQx3MzOivtPt7r427nqSwt3/zt2nu3ua6ILY0+6uFhjg7r8AXjezCzKbLge6YiwpSXYBC82sPvOzdTm62DzaE8ANmcc3AP8VYy1FkchwJ2qhfoKoZdqZuX0o7qIk8T4DtJvZi8Bc4Esx15MImb9mNgJbgZ8S/dwH94nMfJnZBuBHwAVm1mtmfw78I7DUzF4h+kvnH+OssRj0CVURkQAlteUuIiIToHAXEQmQwl1EJEAKdxGRACncRUQCpHAXEQmQwl1EJEAKdxGRAP0/1YKORZnZWoQAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -603,7 +603,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f15ee5f1d68>"
+       "<matplotlib.legend.Legend at 0x7ff9c871af28>"
       ]
      },
      "execution_count": 21,
@@ -641,7 +641,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -695,7 +695,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f15ee5f9748>"
+       "<matplotlib.legend.Legend at 0x7ff9c867b7b8>"
       ]
      },
      "execution_count": 26,
@@ -704,7 +704,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -740,7 +740,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor(413.9844, grad_fn=<MeanBackward1>)\n"
+      "tensor(509.5237, grad_fn=<MeanBackward1>)\n"
      ]
     }
    ],
@@ -769,10 +769,10 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "tensor([[ -34.1391],\n",
-      "        [-146.6133],\n",
-      "        [-215.9149]])\n",
-      "tensor([-27.0838])\n"
+      "tensor([[ -64.6688],\n",
+      "        [ -84.8521],\n",
+      "        [-431.2343]])\n",
+      "tensor([-16.0116])\n"
      ]
     }
    ],
@@ -801,7 +801,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f15ee4ec668>"
+       "<matplotlib.legend.Legend at 0x7ff9c8640320>"
       ]
      },
      "execution_count": 31,
@@ -810,7 +810,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -846,11 +846,11 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "epoch 20, Loss: 73.67843\n",
-      "epoch 40, Loss: 17.97095\n",
-      "epoch 60, Loss: 4.94101\n",
-      "epoch 80, Loss: 1.87171\n",
-      "epoch 100, Loss: 1.12812\n"
+      "epoch 20, Loss: 24.61406\n",
+      "epoch 40, Loss: 5.92470\n",
+      "epoch 60, Loss: 1.55844\n",
+      "epoch 80, Loss: 0.53303\n",
+      "epoch 100, Loss: 0.28755\n"
      ]
     },
     {
@@ -894,7 +894,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.legend.Legend at 0x7f15ee4b1630>"
+       "<matplotlib.legend.Legend at 0x7ff9c8603e10>"
       ]
      },
      "execution_count": 33,
@@ -903,7 +903,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
diff --git a/2_pytorch/PyTorch_quick_intro.ipynb b/2_pytorch/PyTorch_quick_intro.ipynb
index 1f3a5b7..5d84408 100644
--- a/2_pytorch/PyTorch_quick_intro.ipynb
+++ b/2_pytorch/PyTorch_quick_intro.ipynb
@@ -41,14 +41,11 @@
     {
      "data": {
       "text/plain": [
-       "\n",
-       "1.00000e-07 *\n",
-       "  0.0000  0.0000  5.3571\n",
-       "  0.0000  0.0000  0.0000\n",
-       "  0.0000  0.0000  0.0000\n",
-       "  0.0000  5.4822  0.0000\n",
-       "  5.4823  0.0000  5.4823\n",
-       "[torch.FloatTensor of size 5x3]"
+       "tensor([[5.0275e-38, 0.0000e+00, 5.7453e-44],\n",
+       "        [0.0000e+00,        nan, 4.5886e-41],\n",
+       "        [1.3733e-14, 6.4076e+07, 2.0706e-19],\n",
+       "        [7.3909e+22, 2.4176e-12, 1.1625e+33],\n",
+       "        [8.9605e-01, 1.1632e+33, 5.6003e-02]])"
       ]
      },
      "execution_count": 2,
@@ -70,13 +67,11 @@
     {
      "data": {
       "text/plain": [
-       "\n",
-       " 0.3673  0.2522  0.3553\n",
-       " 0.0070  0.7138  0.0463\n",
-       " 0.6198  0.6019  0.3752\n",
-       " 0.4755  0.3675  0.3032\n",
-       " 0.5824  0.5104  0.5759\n",
-       "[torch.FloatTensor of size 5x3]"
+       "tensor([[0.7334, 0.3729, 0.2952],\n",
+       "        [0.0380, 0.1581, 0.2454],\n",
+       "        [0.6000, 0.1633, 0.7892],\n",
+       "        [0.1951, 0.5389, 0.3149],\n",
+       "        [0.6041, 0.8072, 0.5542]])"
       ]
      },
      "execution_count": 3,
@@ -133,13 +128,11 @@
     {
      "data": {
       "text/plain": [
-       "\n",
-       " 0.4063  0.7378  1.2411\n",
-       " 0.0687  0.7725  0.0634\n",
-       " 1.1016  1.4291  0.7324\n",
-       " 0.7604  1.2880  0.4597\n",
-       " 0.6020  1.0124  1.0185\n",
-       "[torch.FloatTensor of size 5x3]"
+       "tensor([[1.7112, 1.2969, 0.3289],\n",
+       "        [0.7841, 1.0128, 0.7596],\n",
+       "        [1.1364, 1.1541, 0.8970],\n",
+       "        [0.8831, 0.7063, 0.3158],\n",
+       "        [1.5160, 1.3610, 0.8437]])"
       ]
      },
      "execution_count": 5,
@@ -182,22 +175,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\n",
-       " 0.4063  0.7378  1.2411\n",
-       " 0.0687  0.7725  0.0634\n",
-       " 1.1016  1.4291  0.7324\n",
-       " 0.7604  1.2880  0.4597\n",
-       " 0.6020  1.0124  1.0185\n",
-       "[torch.FloatTensor of size 5x3]"
+       "tensor([[1.7112, 1.2969, 0.3289],\n",
+       "        [0.7841, 1.0128, 0.7596],\n",
+       "        [1.1364, 1.1541, 0.8970],\n",
+       "        [0.8831, 0.7063, 0.3158],\n",
+       "        [1.5160, 1.3610, 0.8437]])"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -211,7 +202,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -219,32 +210,23 @@
      "output_type": "stream",
      "text": [
       "最初y\n",
-      "\n",
-      " 0.0390  0.4856  0.8858\n",
-      " 0.0617  0.0587  0.0171\n",
-      " 0.4818  0.8272  0.3572\n",
-      " 0.2849  0.9205  0.1565\n",
-      " 0.0196  0.5020  0.4426\n",
-      "[torch.FloatTensor of size 5x3]\n",
-      "\n",
+      "tensor([[0.9778, 0.9240, 0.0337],\n",
+      "        [0.7461, 0.8548, 0.5141],\n",
+      "        [0.5364, 0.9908, 0.1078],\n",
+      "        [0.6880, 0.1675, 0.0010],\n",
+      "        [0.9120, 0.5539, 0.2896]])\n",
       "第一种加法，y的结果\n",
-      "\n",
-      " 0.0390  0.4856  0.8858\n",
-      " 0.0617  0.0587  0.0171\n",
-      " 0.4818  0.8272  0.3572\n",
-      " 0.2849  0.9205  0.1565\n",
-      " 0.0196  0.5020  0.4426\n",
-      "[torch.FloatTensor of size 5x3]\n",
-      "\n",
+      "tensor([[0.9778, 0.9240, 0.0337],\n",
+      "        [0.7461, 0.8548, 0.5141],\n",
+      "        [0.5364, 0.9908, 0.1078],\n",
+      "        [0.6880, 0.1675, 0.0010],\n",
+      "        [0.9120, 0.5539, 0.2896]])\n",
       "第二种加法，y的结果\n",
-      "\n",
-      " 0.4063  0.7378  1.2411\n",
-      " 0.0687  0.7725  0.0634\n",
-      " 1.1016  1.4291  0.7324\n",
-      " 0.7604  1.2880  0.4597\n",
-      " 0.6020  1.0124  1.0185\n",
-      "[torch.FloatTensor of size 5x3]\n",
-      "\n"
+      "tensor([[1.7112, 1.2969, 0.3289],\n",
+      "        [0.7841, 1.0128, 0.7596],\n",
+      "        [1.1364, 1.1541, 0.8970],\n",
+      "        [0.8831, 0.7063, 0.3158],\n",
+      "        [1.5160, 1.3610, 0.8437]])\n"
      ]
     }
    ],
@@ -306,22 +288,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\n",
-       " 1\n",
-       " 1\n",
-       " 1\n",
-       " 1\n",
-       " 1\n",
-       "[torch.FloatTensor of size 5]"
+       "tensor([1., 1., 1., 1., 1.])"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -333,7 +309,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -342,7 +318,7 @@
        "array([1., 1., 1., 1., 1.], dtype=float32)"
       ]
      },
-     "execution_count": 11,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -354,7 +330,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [
     {
@@ -362,14 +338,7 @@
      "output_type": "stream",
      "text": [
       "[1. 1. 1. 1. 1.]\n",
-      "\n",
-      " 1\n",
-      " 1\n",
-      " 1\n",
-      " 1\n",
-      " 1\n",
-      "[torch.DoubleTensor of size 5]\n",
-      "\n"
+      "tensor([1., 1., 1., 1., 1.], dtype=torch.float64)\n"
      ]
     }
    ],
@@ -424,15 +393,28 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "tensor([[2.4446, 1.6699, 0.6242],\n",
+      "        [0.8222, 1.1709, 1.0050],\n",
+      "        [1.7364, 1.3174, 1.6862],\n",
+      "        [1.0782, 1.2452, 0.6307],\n",
+      "        [2.1201, 2.1682, 1.3979]], device='cuda:0')\n"
+     ]
+    }
+   ],
    "source": [
     "# 在不支持CUDA的机器下，下一步不会运行\n",
     "if t.cuda.is_available():\n",
     "    x = x.cuda()\n",
     "    y = y.cuda()\n",
-    "    x + y"
+    "    x + y\n",
+    "print(x+y)"
    ]
   },
   {
@@ -459,7 +441,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -468,7 +450,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 19,
    "metadata": {
     "scrolled": true
    },
@@ -476,13 +458,11 @@
     {
      "data": {
       "text/plain": [
-       "Variable containing:\n",
-       " 1  1\n",
-       " 1  1\n",
-       "[torch.FloatTensor of size 2x2]"
+       "tensor([[1., 1.],\n",
+       "        [1., 1.]], requires_grad=True)"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -495,7 +475,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 20,
    "metadata": {
     "scrolled": true
    },
@@ -503,12 +483,10 @@
     {
      "data": {
       "text/plain": [
-       "Variable containing:\n",
-       " 4\n",
-       "[torch.FloatTensor of size 1]"
+       "tensor(4., grad_fn=<SumBackward0>)"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -520,16 +498,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<SumBackward0 at 0x7fc14824b860>"
+       "<SumBackward0 at 0x7fe8cf72c908>"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -540,7 +518,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -549,19 +527,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Variable containing:\n",
-       " 1  1\n",
-       " 1  1\n",
-       "[torch.FloatTensor of size 2x2]"
+       "tensor([[1., 1.],\n",
+       "        [1., 1.]])"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -581,19 +557,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Variable containing:\n",
-       " 2  2\n",
-       " 2  2\n",
-       "[torch.FloatTensor of size 2x2]"
+       "tensor([[2., 2.],\n",
+       "        [2., 2.]])"
       ]
      },
-     "execution_count": 21,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -631,19 +605,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "\n",
-       " 0  0\n",
-       " 0  0\n",
-       "[torch.FloatTensor of size 2x2]"
+       "tensor([[0., 0.],\n",
+       "        [0., 0.]])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -655,19 +627,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 24,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "Variable containing:\n",
-       " 1  1\n",
-       " 1  1\n",
-       "[torch.FloatTensor of size 2x2]"
+       "tensor([[1., 1.],\n",
+       "        [1., 1.]])"
       ]
      },
-     "execution_count": 24,
+     "execution_count": 26,
      "metadata": {},
      "output_type": "execute_result"
     }
diff --git a/README.md b/README.md
index cfa4ffe..545ed1f 100644
--- a/README.md
+++ b/README.md
@@ -1,8 +1,8 @@
 # Python与机器学习
 
-本教程包含了一些使用Python来学习机器学习的notebook，通过本教程能够引导学习Python的基础知识、机器学习的理论知识与实际编程，并学习如何解决实际问题。
+本教程包含了一些使用Python来学习机器学习的notebook，通过本教程的引导来快速得学习Python、Python的常用库、机器学习的理论知识与实际编程，并学习如何解决实际问题。
 
-由于**本课程需要大量的编程练习才能取得比较好的学习效果**，因此需要认真把作业和报告完成。作业的地址是：https://gitee.com/machinelearning2018/pr_homework 请按照里面的说明进行操作，并提交作业。
+由于**本课程需要大量的编程练习才能取得比较好的学习效果**，因此需要认真把作业和报告完成，写作业的过程可以查阅网上的资料，但是不能直接照抄，需要自己独立思考并独立写出代码。
 
 
 ## 内容