diff --git a/doc/pub/week39/ipynb/week39.ipynb b/doc/pub/week39/ipynb/week39.ipynb
index ab8fd9e22..f3b8506b5 100644
--- a/doc/pub/week39/ipynb/week39.ipynb
+++ b/doc/pub/week39/ipynb/week39.ipynb
@@ -782,9 +782,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "%matplotlib inline\n",
@@ -821,9 +819,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pt.axis(\"equal\")\n",
@@ -841,9 +837,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = guesses[-1]\n",
@@ -860,9 +854,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "def f1d(alpha):\n",
@@ -884,9 +876,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "pt.axis(\"equal\")\n",
@@ -1262,9 +1252,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "x = 2*np.random.rand(m,1)\n",
@@ -1430,11 +1418,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.32017409 4.33901633]\n",
+      "[[4.35923237]\n",
+      " [2.73841882]]\n",
+      "[[4.35923237]\n",
+      " [2.73841882]]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "\n",
     "# Importing various packages\n",
@@ -1494,9 +1504,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Importing various packages\n",
@@ -1579,11 +1587,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "collapsed": false
-   },
-   "outputs": [],
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[[4.16247703]\n",
+      " [2.89165934]]\n",
+      "[[4.15521623]\n",
+      " [2.89490741]]\n"
+     ]
+    },
+    {
+     "data": {
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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "from random import random, seed\n",
     "import numpy as np\n",
@@ -1601,6 +1630,7 @@
     "X = np.c_[np.ones((n,1)), x]\n",
     "XT_X = X.T @ X\n",
     "\n",
+    "\n",
     "#Ridge parameter lambda\n",
     "lmbda  = 0.001\n",
     "Id = lmbda* np.eye(XT_X.shape[0])\n",
@@ -1610,8 +1640,10 @@
     "# Start plain gradient descent\n",
     "beta = np.random.randn(2,1)\n",
     "\n",
+    "\n",
+    "\n",
     "eta = 0.1\n",
-    "Niterations = 100\n",
+    "Niterations = 1000\n",
     "\n",
     "for iter in range(Niterations):\n",
     "    gradients = 2.0/n*X.T @ (X @ (beta)-y)+2*lmbda*beta\n",
@@ -1763,9 +1795,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np \n",
@@ -1827,9 +1857,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np \n",
@@ -1868,9 +1896,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "# Importing various packages\n",
@@ -2423,9 +2449,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2481,9 +2505,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2519,9 +2541,7 @@
   {
    "cell_type": "code",
    "execution_count": 15,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2572,9 +2592,7 @@
   {
    "cell_type": "code",
    "execution_count": 16,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2614,9 +2632,7 @@
   {
    "cell_type": "code",
    "execution_count": 17,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2648,9 +2664,7 @@
   {
    "cell_type": "code",
    "execution_count": 18,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2710,9 +2724,7 @@
   {
    "cell_type": "code",
    "execution_count": 19,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2736,9 +2748,7 @@
   {
    "cell_type": "code",
    "execution_count": 20,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2785,9 +2795,7 @@
   {
    "cell_type": "code",
    "execution_count": 21,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2815,9 +2823,7 @@
   {
    "cell_type": "code",
    "execution_count": 22,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2845,9 +2851,7 @@
   {
    "cell_type": "code",
    "execution_count": 23,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "import autograd.numpy as np\n",
@@ -2877,9 +2881,7 @@
   {
    "cell_type": "code",
    "execution_count": 24,
-   "metadata": {
-    "collapsed": false
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "a += b\n",
@@ -2889,7 +2891,25 @@
    ]
   }
  ],
- "metadata": {},
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
  "nbformat": 4,
  "nbformat_minor": 4
 }