diff --git a/doc/HandWrittenNotes/NotesAugust21.pdf b/doc/HandWrittenNotes/NotesAugust21.pdf
new file mode 100644
index 000000000..37e625a71
Binary files /dev/null and b/doc/HandWrittenNotes/NotesAugust21.pdf differ
diff --git a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
index 4f41667fa..c870d372f 100644
--- a/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
+++ b/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb
@@ -430,8 +430,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "import nump\n",
-    "y as np"
+    "import numpy as np"
    ]
   },
   {
@@ -443,16 +442,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 39,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "[ 0.62788768  0.95270524 -0.82962774  1.16046637 -0.31152511 -0.53939102\n",
-      "  0.54043276  0.14069506  1.23826241 -1.28557386]\n",
-      "0.9527052435093047\n"
+      "[-1.40156441 -0.28390886 -0.77030319  1.72164112 -0.05756168 -0.77474089\n",
+      "  1.24566209  1.96410269  0.04055138  0.38110718]\n",
+      "-0.2839088573652084\n"
      ]
     }
    ],
@@ -1343,7 +1342,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1623,6 +1622,23 @@
      },
      "metadata": {},
      "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          0         1         2         3         4\n",
+      "0 -1.574465  0.259153  1.197370  0.147400  0.649382\n",
+      "1  0.689519  0.137652 -1.025709  0.210340 -0.076938\n",
+      "2 -0.282727  0.351636 -0.539261  1.216683  0.340782\n",
+      "3  0.070889 -0.614808  1.074067 -0.038300 -1.450257\n",
+      "4  1.794282  1.458078 -0.207545 -0.442600 -0.147420\n",
+      "5  1.112383  0.647473  1.405890  0.073598 -0.276263\n",
+      "6  0.397700 -1.526744 -0.712018  1.216290  0.418506\n",
+      "7 -0.280647  1.106095 -1.646283 -0.956563 -1.564374\n",
+      "8 -0.369139 -0.751699  0.051649 -0.213103  0.967809\n",
+      "9 -1.557795 -1.066837  0.401842 -1.213743  1.138775\n"
+     ]
     }
    ],
    "source": [
@@ -1638,7 +1654,8 @@
     "display(df)\n",
     "print(df.mean())\n",
     "print(df.std())\n",
-    "display(df**2)"
+    "display(df**2)\n",
+    "print(df-df.mean())\n"
    ]
   },
   {
@@ -1996,12 +2013,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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+      "image/png": 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ALIwgBwDAwghyAAAsjCAHAMDCCHIAACyMIAcAwMIIcgAALIwgBwDAwghyAAAsjCAHAMDCghbkZ86c0e23364pU6YE65IAANR7QQvy6dOnKz4+PliXAwCgQQhKkC9cuFBXX3214uLignE5AAAajIAH+Y8//qg9e/bolltuCfSlAABocGzGGBPIC7z55pvyeDwKDQ3V6tWrVVBQoJtvvln33ntvIC8LAECD4Az0Be677z7fz/n5+crNza1UiB87djqApUJMTGPqOMCo48CjjoODeg68mJjG1T42aIPdlixZovXr1+u7777Tl19+GazLAgBQrwW8RV6kf//+6t+/f7AuBwBAg8CEMAAAWBhBDgCAhRHkAABYGEEOAICFEeQAAFgYQQ4AgIUR5AAAWBhBDgCAhRHkAABYGEEOAICFEeQAAFgYQQ4AgIUR5AAAWBhBDgCAhRHkAABYGEEOAICFEeQAAFgYQQ4AgIUR5AAAWBhBDgCAhRHkAABYGEEOAICFEeQAAFgYQQ4AgIUR5AAAWBhBDgCAhRHkAABYGEEOAICFEeQAAFgYQQ4AgIUR5AAAWBhBDgCAhRHkAABYGEEOAICFEeQAAFgYQQ4AgIUR5AAAWBhBDgCAhRHkAABYGEEOAICFEeQAAFgYQQ4AgIUR5AAAWBhBDgCAhRHkAABYGEEOAICFEeQAAFgYQQ4AgIUR5AAAWBhBDgCAhRHkAABYGEEOAICFOQN9Aa/Xqz/84Q/q1KmTCgoKtH//fr344otq1KhRoC8NAEC9F5QWeZcuXTR27Fg9+uijysvL09KlS4NxWQAA6r2AB7ndbtf9998vSXK73Tpy5IguvfTSQF8WAIAGwWaMMcG40MqVK/Xuu++qc+fOeuihh4JxSQAA6r2gBXmRJ554Qp07d9aIESPK3e/YsdNBKlHDFBPTmDoOMOo48Kjj4KCeAy8mpnG1jw141/qPP/6o1NRU3+u4uDgdOHAg0JcFAKBBCPio9dDQUM2fP18//PCD3G63du/erWeeeSbQlwUAoEEIeJC3adNGM2fODPRlAABokJgQBgAACyPIAQCwMIIcAAALI8gBALAwghwAAAsjyAEAsDCCHAAACyPIAQCwMIIcAAALI8gBALAwghwAAAsjyAEAsDCCHAAACyPIAQCwMIIcAAALI8gBALAwghwAAAsjyAEAsDCCHAAACyPIAQCwMIIcAAALI8gBALAwghwAAAurMMiXLl0ajHIAAIBqqDDIP/30UyUlJSk9PT0Y5QEAAFVQYZDPmTNHgwYN0r333qvZs2fL7XYHo1wAAKASKnWPfODAgfrss8909OhRDRkyRBs2bAh0uQAAQCVUKsh37typxYsXKzs7W0eOHNGYMWM0ceJE5eXlBbp8AACgHBUGebdu3fTwww9r8+bNuu666zR//nxt2LBBl112mR566KFglBEAAJTBWdEOS5cuVXR0dKn3R44cqU8//TQghQIAAJVTYYvcX4gXmTlzZo0WBgAAVM15TQhz2WWX1VQ5AABANTCzGwAAFkaQAwBgYQQ5AAAWRpADAGBhBDkAABZGkAMAYGEEOQAAFkaQAwBgYQQ5AAAWRpADAGBhBDkAABZGkAMAYGEEOQAAFkaQAwBgYQQ5AAAWRpADAGBhBDkAABZGkAMAYGEEOQAAFkaQAwBgYc5AX2Dfvn2aPn264uPjdfjwYTVt2lRjx44N9GUBAGgQAh7kJ0+e1MCBA9WvXz9J0sCBA9WnTx8lJCQE+tIAANRZYSnzFT79VTl2bpfi46Xvv6/WeQIe5J06dSrx2uv16oILLgj0ZQEAqBN8gb1jmxQSIrlc8rZqLUf6gZ922ry52ue3GWNMDZSzUv7xj38oLS1NzzzzTLAuCQBA7fn4Y2n48MrtW804DniLvMjatWuVlpamp556qlL7Hzt2OsAlathiYhpTxwFGHQcedRwc1HPVlOgydzplC/D1ghLkqamp2rBhg55++mkdPXpU6enp6tq1azAuDQBAwPnCe/sPshVvWXs8Ab92wIN8y5YtevTRR5WQkKC7775bubm5GjFiBEEOAKgXwuZ/oqj7f19r1w94kCckJOjbb78N9GUAAKhRxbvIPW3bK/eRx5Q/eKhkjBx7flTI8lSFrkhV6P8trvY1PHFxsh8+LFt8fLXPEbR75AAAWEVYynxFJY3yvXZu26qopFFyvfe2HP/dK8fBn0acV2aImglrJBUUFI5aL3DJ0z5euQ//sfAfBioch1BdBDkAoMEofi9boaFSQYE87Tr81No+K2LyRL/Hh67+t7zNmunMLweroFcfuXr1UZN7fivntq3lXvf0jL+WOH9NIsgBAPVamQPR8vMl/dTazt6zWzabTSErUmVPP+j3XEZSxg97JIfD917uI4+VaL379rU75GnfoUTLOxAIcgBAvXVuF3l5Iqf8SZJkbBU8MFYsxCUpf/BQZUkKf23aT/fTAxzexRHkAIB6K3z6q5Xe19hsyprzrgp69lKzG28oOfPaWd7WcX6PzR88NGjBfS5WPwMAWFpYynxFd41Xiwuj1OLCKEV3iVdYynwpJ6dwWtRK8nToKNcvB8tEN1fOpOf87pOT7P/92kSLHABgSWEp8xUxOblUy9mRfkBRSaNkHA7ZvN5Kny/34T/6fq7t7vKqoEUOAKhzwlLmq1nvHmrRqpma9e5R2MI+Z3tU0ii/3d8+ISHK7z+wzM3GZpOxO+SOT1DWrLdLhXT+4KE6kbpax9MzdSJ1dZ0McSnIi6ZUBfP6BhZzJwcedRx41HFwBLueyxqg5mkdJ/vhQ/K2bClb5gnZ83LLPY9xOnU8PVMRT41T+NxZpbb7C+/acj7PkdMiBwDUKWUNUHMcPCCbxyPHwYMVhrgkedq2lyTlvPiKsma9LXd8gozTWWYL3Kq4Rw4AqDvc7ioNUCvPufe860twn4sgBwAE1blzmJ+5c4RMaKhCV6QqZNXKKg1QO5dR4SNiOcnP1dvgPhdBDgCoUWUuNiL/c5hHTnrK99pzyc/k6txVoStTK7yOJy5OJqppnR9VHmgEOQCgxpS12Ejulu+lArcueGeO3+M8rS7Syc+/kvdnl/rOU/Tolze2ZYlFSorkTGw4re7yMGq9gWK0b+BRx4FHHQdHVeq5We8e5S4gYiT5mwC1aIR5WYoHe31sfbP6GQCg9hgjx+4fFbJ8WeGqYv52sdt16u8LFfnMeDm3lx7MVjTCvCz1ebDa+SLIAQBVZjt6VKErUxWyIlWhK1L9dn0X52kfr4JefZT76Di/z4gXH2GOqiHIAQAVy85WaNpqhaQuU+iK1BLd597oaJ0ZNEQFvfpIZ/LU+OnxpQ4vCmorTX1qFQQ5AKA0t1vO775R6PJl0pqVarFmjWwFBZIk06iRXL37ytWrrwp695E7oZNk/2l+MdMiptygppu8ZhHkANDAFC02Yj87T7m3dZxyJk6Wu1MXhSz/l0KXFz7PbT+dVXiAzSZ35y4q6NVXrl59VJB4ndSoUZnnJ6iDiyAHgAbE3zzmjoMHFPWH35V4z/OzS5U3eKhcvfuoyR236qQnJJjFRBUQ5ADQUGRnK+KFZ8vc7I1qopxJz8vVq4+8l/zspw3RjSUe86uzWDQFAOoBv8t+ut1yrk9T+KtT1GTQL9Si3SVy7N9X5jlsuTk6c9e9JUMcdR4tcgCwAN+0p9t/kBwOye2WVHh/O/8Xt5ZYprNoNjXvww/IfiZPUuHa2+7OXeTYv1/2jON+r1HRs9yomwhyAKjjSt3XPhviUuH9bX9rbUuSzeNR3t2j5OrdVwU9b5BpFl3mWt8Sz3JbFUEOAHVcxHPJ1TvQeJX95+kl3ip6jjviuWTZz07i4o2LY95yCyPIAaAOKGvFsLD5n1Q4a1pZyuoq5/Gw+oUgB4BaVtaKYQV/fV3OzZuqfV66yhsGRq0DQID5HVFeTPifp/g9LmTTt5LXW6lr5I5Okjs+QcbplDs+QVmz3qbV3UDQIgeAACpzfe7N30sFBQpdsUzOXTv8HmscTnnatvO7LGjR+tPF72/nBOIDoM4jyAGgBkU8NU4XzHtXys+XwsJknP5nRAufWTgIzTRqJG9EhOw5pWPY065wnnJ/o8xP0+LGWXStA8B58nWdxzZR+NxZsuXnyybJlp8ve06232OMpJMLFun4zn3Knva6332KFhvJmvU23eYoEy1yADgP5T2XXZGCG3pLqnhpT0aZozwEOQBUR3a2QteuUuSTj1frcG/ruBKvCWtUF0EOAJXhdsv57UaFLl+mkBWpCtmwTrZiM6xVVU7yczVYODRkBDkA+GOMHD/uKlyfe0WqQlb927c+t7HZ5O7SVQW9+ips4QI5/ru33FPl971JjiNH/HabA+eLIAdQ75U1a9q5bEeOKHTFssLgXpEqx6F03zb3pZcpf8iwwnnLr+8p0yy68P34jn7vkRtJCmukvLvuUc6LrwTqowEEOYD6raznuLMk5d88QKFrVylkeWF4O7f94NvP27y5ztwxRAW9b5Trht7ytrnE7/krGqgGBJrNGGMq3i34jrGIfUDFxDSmjgOMOg68iuo4LGW+Gj+QJJu7oNQ2b3i4bC6X7z63adRIBdf9XK7eN8rVq488HRMkO0/oSnyXgyEmpnG1j6VFDsDSSqzTHRoqFRTI066DPC1bKmzZP8s8zpabK3fXq+XqfaMKevVRQbdEqVGjIJYcqBkEOQBLCkuZL70wSVH79//0Zn6+pMLuc3/TmhbnaddBJ5ekBrCEQHAQ5AAsofiANW9sKznSq7e0Z5HcP46roZIBtYsgB1CnhaXMV8RzySXW5D7fEPe0jmMwGuoNghxAnVP8vrctAONxmYwF9QlDMgEEXXnrcxc9LubctrXGQ9wbHc2CI6h3aJEDCKqy1+feJBUU6IJ3/3be1/C0jlP+L25V6OpVPNuNeo8gBxBU4dNf9f/+zNcknZ0RrbLatJE5cEAKOfvYWfsOJQK79ArfQP1DkAMIPGPk2LVTISuWFT7v7W8Xu12nFixS5JPj5CxjnyKe1nHKSX5OUWNG6jgTlaCBI8gBnJey5jG3HzmskBWpvtXCHIcPlXseT/t4FVx/g3Iffdz/3OV2R6kWNwCCHMB5KOt+t2fyM3Kk/7TgiLdFC50Z/CsV9Oor5eWq8VNPlDpX7sN/lMTc5UBVEeQAylXmymEFBQp/6Xm/x9gPHZKr700/zVse37HEvOWmeYtygzp/8FCCG6gkFk1poFgEIfCsWsclZlBr2arERCxFCjp1lmPPHtmz/X8+43TqeHpmoItq2Tq2Guo58Or0oinHjh3T9OnTtX37di1YsCDQlwNQReVNfeovxCUp5PtNcl92uRTilP3EiVLbPW3bB6y8AEoK+IQwGzdu1E033aQ62vAHGrQSk694PJWe+tQ4nDqx9ltlv+z/UbKi+90AAi/gLfIBAwYoLS0t0JcBUIZz73G7ru+p0FX/lmPndslZvT8BnnaFLW4GpgG1Lyj3yNPS0jRlyhR99tlngb4UgOI+/lgaPrzmz/vRR9Kdd9b8eQFUWZ0dtc7AisBi8Erg1YU6bvbcCzXyP7mndZzsRw7/1OK+6VapDnx/6kIdNwTUc+DV6cFuAGpWWMp8RUxOlv3s/Wzv2VnOirqzbdmnFbL634WTsGzbWu3rGEme+AS6yoE6LuBBvm7dOn3++ec6duyY/vrXv2rUqFFq1KhRoC8L1Cu++9w7tsnm9ZbY5jh4QFFJo3Rm0edyHDsq58b1srndkiRjs0nVvHt2mlXCAEsIeJAnJiYqMTEx0JcB6g1/g9PC586q8LhGX34uY7fL3fVquXr1UUGvvrIfPKiosWMqPDZ3dBIrhQEWRdc6UEcUdZkXfwTMuW2rnJXsHjeSMnbslWnStMT7WSHOwlHl27fJ5vWUOi53dJJyXnyFlcIAiwr4c+QAKlb0PHdln+Muy7khLhU+InYidbWOHz6hrFlvyx2fION0yh2foKxZbyvnxVfO65oAahctciDAypyrvJjw6X8+7+t4W8dVuA9zmAP1D0EOBFBZq4NlGSN3x6sUumKZQpYvk2Nb+etvV0ZO8nPnfQ4A1kOQAwEUPt3/FKaNxyb5RpZLkgkNlVyuKp3bExcn++HDDE4DGjjukQPnKSxlvpr17qEWrfZde7EAAA5JSURBVJqpWe8eCkuZX7jh9Gk5dmzzf5DbrTNDhirrtb8q45utOv36W35388TFKWvW237vbWd+84OOp2fqROpqQhxowFjGtIFipqaacW7XeRH35VfI+d+9UrFWd4nt8R11InVNqXMxZ3nV8D0ODuo58M5nZjda5EAFymxxG6PwKS/6Pcax+0fpmmt0ZuBtfrfnPvxYqfd8o8tpZQOoAlrkDRT/wq6cslrcru495Nj7HzmOHPZ7nHE6ZSso0LFjp2lpBxDf4+CgngOPudaBAAmf5v8Z69C0NfK2iJE3qonsWadKbfe0be/7n4tHvgAEEl3raJDK7C4vKJBz7RqFT31RTW+7pczBasbhUMaWXcp+5S9+t+c+/MdAFR0ASqBFjganrGe7C2a+Jsee3bLnZEuSjN0uXXCBlJdX6hyedh0ku135g4cqS6LrHECtIchRr4SlzFfEc8myH/xpqtNzl/kM//PLfo8N2bxJ7iuuVH6vPnL16quC63sq9F9f+71HXrzFTdc5gNpEkKNeCEuZr8gJj8t+IrPUtqJlPvM/+V85Dh6Qc9dOv+cwTqdOrN5Y4j1a3ADqOoIcllXeGt1+9//X1zLh4fJGRsqenV1qu6dte7/H0eIGUJcx2A2WE5YyX9Fd4xWVNErObVsrFeJS4TKfx3fuU/arM/xuZ4AaACuiRY46ydfa3v6DFBoqFRTI066DXNf3VPjcWdU/cWgo3eUA6hWCHHVOqUlY8vMlFY4ud27bWu3zeuN+WuaT7nIA9QVBjqA7d31u1/U9Fbrq377XtlOlJ1ipCTkTWeYTQP1DkCMoineV24rNCnxuK/t8WtxS4X1wSZLDIXk8kgpb4jkTn6MFDqBeIshR44q3uL2xrWTLy/X7WFhN8pzzrDgANBQEOWrUufe3HekHytm7enJHJyl09SoGqgGACHLUsPDpr9bIeTxxcTJRTQtHrYecHbXevoMvtHNq5CoAYH0EOWqOMXLs3F4jp+KeNgBUDkGO82I/lK6Q5csUuiJVIStSZTs7wKwyjN0hT/sOcv38errKAaCaCHKUcO6jYbmPPFYiVG2nsxSy6t8KWVEY3s6dO3zbvDEXypV4nULXrS33Gh4/o8jpKgeA6iHIG7jiwW2impQYXV60vGfOrp2SMYXB/c0GX6vbhEcov98tKji7WpinQ7xksxUuYPLk47Jnlh6pnt/3JmV9khK0zwcA9Z3NmGIP9dYhx46dru0i1GsxMY2VNfsdv0t0lsU4HHJ3vUauXn1U0LuvCq65tnD61DJEPDVOF8x7T8o/I4U1Ut5d9yjnxVdqoviWEBPTmO9xgFHHwUE9B15MTONqH0uQNyDFW9+2+Hh5Mk5U6vEwY7Mp693/VcH1PWWimgShpPUDf/wCjzoODuo58M4nyOlabyBKzV++ebMclTzW06GjXL+4NSDlAgCcH5YxrWfCUuarWe8eatGqmZr17qGwlPmSy6XIp5+o9jlZ3hMA6i5a5PXIua3uosFqJjRMNld+tc6ZOzqJR8EAoA6jRW5B/lrd9vSDipg8sYwjjMoaCGEkueMTZJxOeVrHFc6o5nTKHZ+grFlvN6jBaQBgRbTILaasVne5vF55W8fJcbD0wDZvXJxOpK6u6WICAIKEFnkdFJYyX9Fd49Xiwii1uDBK0V3iFfbpxwpZu1qRT4/3e4w3MlKe2JZ+t3natldOsv+1uFmjGwCsjRZ5HXDusp/nPhLmSD+gqAfGlHsO25kzyn51ht/WedGUp1mSwl+b5nv8LOuBR7j/DQAWR5DXsqos++mNjpaJiJRj/75S2zxt25cK63PnLc8fPNT3c0xMY+XzXCgAWB5d60Hg95EwSbasU4p44dlKn8eWlaWcZ/zvX/SIWP7goTqRulrH0zN1InU1LW4AqOdokQdYxFPjFD53lu910eA095Q/yfHfvVVaLawyrW4AQMNCkAdQ2Geflgjx4hx7dst9bXc5/rNH9uPHKnW+4q1ughsAING1XmVldZMXsacfVNjHH6rxfaPVuLwBak6nTi7+h7L/NMXvZm90tIwKn/P2tI5T1qy3CW8AQCm0yKugrGe4c7/7VjZXvkJWpMq5a6dve3mr0XjatpckusoBAOeFIC+m+GNgnrbtlfvIYyUCNXz6q36PC3/zdUln1+e+uX/h+ty9b1RU0ig5t231e0zx+cvpKgcAVBdBflZZre0sY+RuH6/QFcvk2P6D32ON3a6TC7+S++prSqzPnfvIY/6f62b+cgBADWlQQV5ei7us1nbjB5Jk87jLPa+nfbzc1/Uo9T7d5gCAQGswQV5mi1uS66ab5dixzf+BHrfODP2NXL37ypaTo8YTHiu1S3nLfNJtDgAIpAYT5GW2uB95QHK5ZPN6/W73xCfo9F/n+F6bZs1oYQMA6ox68/hZuY+FGSPHju3+D8zLk/uaa3Xmtl/63Xxua5uZ0wAAdYklgryiZ7cjnhrnGyFu83h83ebNL22lJrferOYJV8rm9T+Dmqd9B538cqlOv/2Bsma97Vubu2g9boIaAFCX1fmu9fLubecPHlo4gK2M2dPsOTkKXZ8mb5MmcnXvodC0NaX2yX10nO9n7mcDAKymzrfIy3x2e/qrClmzSpFPP1HhObytL9apRUtocQMA6h2bMaa8CchqzbGzS2y2aNXM78IiRpKtkucyTqeOp2fWXOHqgZiYxr46RmBQx4FHHQcH9Rx4MTGNq31sULrWV69eraVLl6p58+ay2WwaO3ZspY6zHzwg74WxchxKL70xLEy5d49U2P/9P7/rcxdXNB0qAAD1TcCDPC8vT5MmTdLixYsVGhqqBx98UGvWrFGPHqUnUPEZO1bNliyV88ddZe5yesabyh88VO5uiX5nTyuuvOe8AQCwsoDfI//uu+900UUXKfTs1KVXX321UlNTyz/ojTdkP3RI+bcMUPYLLyt70vNl3tvOHzz0p3vfdruMM4RVwwAADUbAW+QZGRmKiIjwvY6MjFRGRkb5Bxkju6Sws/9Jkp59RlJhgaPO3X/MyML/zuHwty98zueeDCqHOg486jg4qOe6K+At8ubNmysnJ8f3Ojs7W82bNw/0ZQEAaBACHuRdunRRenq6XC6XJOmbb75Rnz59An1ZAAAahKA8frZq1SotWbJEzZo1U0hISKVHrQMAgPLV2efIAQBAxer8zG4AAKBsBDkAABZWa4umVDTbW35+vqZMmaLY2Fjt3btXY8aM0aWXXlpLpbWuiup59uzZOn78uFq0aKGtW7fqoYce0uWXX15LpbWmys5c+MUXX2jcuHH65ptvSjySiYpVVMfGGM2bN0+SdPDgQWVlZemll16qjaJaVkV1vH//fk2dOlVXXXWVtm3bpttuu0033XRTLZXWmo4dO6bp06dr+/btWrBgQant1c49Uwtyc3NNv379TH5+vjHGmLFjx5rVq1eX2GfWrFlm9uzZxhhjtm/fboYPHx70clpdZer5L3/5i/F6vcYYYxYvXmySkpKCXk4rq0wdG2PMjz/+aKZNm2batm1rsrOzg11MS6tMHaekpJiUlBTf623btgW1jFZXmTpOTk4277zzjjHGmK1bt5qbb7452MW0vK+++sr885//NIMHD/a7vbq5Vytd65WZ7S01NVVdu3aVJLVr107bt29XdnZ2sItqaZWp50ceeUQ2W+HyM16vV+Hh4cEupqVVpo7z8vI0d+5cPfDAA7VQQuurTB0vWrRIJ0+e1Pvvv69p06bR41FFlanjFi1aKDOzcPGpzMxMdezYMdjFtLwBAwaU+92sbu7VSpBXZra3as0IhxKqUocul0spKSl65JFHglW8eqEydfyXv/xF999/v++PJKqmMnWcnp6u7Oxs3X333Ro8eLBGjx4tj59VE+FfZep45MiR2rRpk1566SW98cYbGjJkSLCLWe9VN/dq5R55ZWZ7Y0a481fZOnS5XHr22Wf16KOPqk2bNsEsouVVVMeHDh1SVlaWvvrqK99777zzjnr37q2rrroqqGW1qsp8jyMjI9W5c2dJ0qWXXqrs7GwdOnRIcXFxQS2rVVWmjidMmKBhw4bptttuU2Zmpm655RZ9/fXXatq0abCLW29VN/dqpUVe1mxvJ0+e9HUj9OnTR99++60kaceOHWrfvr0iIyNro7iWVZl6PnPmjCZNmqSRI0cqISFBS5Ysqc0iW05FddyqVSu9/PLLGjNmjMaMGSOpsGVDiFdeZb7HPXr00P79+yUV/vHzeDyKiYmptTJbTWXq+NChQ746jYqKkt1ul9frrbUy1xc1kXu1NiGMv9nepk6dqqZNm2rMmDE6c+aMpkyZopiYGO3bt09JSUmMWq+Giup57Nix2rVrly688EJJUm5urt/RlChbRXUsFd5T/Pjjj/Xaa6/p/vvv15133qnY2NhaLrl1VFTHp0+f1iuvvKKLLrpI+/btU//+/dW7d+/aLralVFTHGzZs0Pvvv6/4+HgdOHBAHTt21PDhw2u72Jaybt06LVy4UCtXrtTw4cM1atQozZgx47xzj5ndAACwMCaEAQDAwghyAAAsjCAHAMDCCHIAACyMIAcAwMIIcgAALIwgBwDAwghyoIF78sknNXPmTEnS3r171b9/f23durWWSwWgspgQBmjgjhw5oiFDhmjOnDl6/PHH9dxzz6lbt261XSwAlVQri6YAqDtiY2N1xx13aMSIEZoxYwYhDlgMXetAA5eRkaEVK1boggsuUKtWrWq7OACqiK51oAHLysrSvffeq6SkJGVmZmr58uV66623artYAKqAFjnQQOXl5SkpKUnDhw9X//79NWzYMO3du1dr166t7aIBqAJa5AAAWBgtcgAALIwgBwDAwghyAAAsjCAHAMDCCHIAACyMIAcAwMIIcgAALIwgBwDAwv4/RCFxUUE29loAAAAASUVORK5CYII=\n",
       "text/plain": [
        "<Figure size 576x396 with 1 Axes>"
       ]
@@ -2017,7 +2034,7 @@
     "from sklearn.linear_model import LinearRegression\n",
     "\n",
     "x = np.random.rand(100,1)\n",
-    "y = 2*x+np.random.randn(100,1)\n",
+    "y = 2*x+0.01*np.random.randn(100,1)\n",
     "linreg = LinearRegression()\n",
     "linreg.fit(x,y)\n",
     "xnew = np.array([[0],[1]])\n",
@@ -2588,7 +2605,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2636,7 +2653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2697,7 +2714,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2739,7 +2756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -2783,7 +2800,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2805,7 +2822,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 37,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -2823,7 +2840,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
diff --git a/doc/pub/Regression/ipynb/Regression.ipynb b/doc/pub/Regression/ipynb/Regression.ipynb
index 699c502c8..88339e687 100644
--- a/doc/pub/Regression/ipynb/Regression.ipynb
+++ b/doc/pub/Regression/ipynb/Regression.ipynb
@@ -6367,7 +6367,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.6"
+   "version": "3.8.3"
   }
  },
  "nbformat": 4,