diff --git a/doc/pub/week40/html/._week40-bs012.html b/doc/pub/week40/html/._week40-bs012.html
index efcfdd25b..59ac9c0b4 100644
--- a/doc/pub/week40/html/._week40-bs012.html
+++ b/doc/pub/week40/html/._week40-bs012.html
@@ -319,11 +319,11 @@ MathJax.Hub.Config({
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> SGDRegressor
 
-n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
+n <span style="color: #666666">=</span> <span style="color: #666666">1000</span>
 x <span style="color: #666666">=</span> <span style="color: #666666">2*</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>rand(n,<span style="color: #666666">1</span>)
 y <span style="color: #666666">=</span> <span style="color: #666666">4+3*</span>x<span style="color: #666666">+</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randn(n,<span style="color: #666666">1</span>)
 
-X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c_[np<span style="color: #666666">.</span>ones((m,<span style="color: #666666">1</span>)), x]
+X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c_[np<span style="color: #666666">.</span>ones((n,<span style="color: #666666">1</span>)), x]
 theta_linreg <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linalg<span style="color: #666666">.</span>inv(X<span style="color: #666666">.</span>T <span style="color: #666666">@</span> X) <span style="color: #666666">@</span> (X<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y)
 <span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Own inversion&quot;</span>)
 <span style="color: #008000">print</span>(theta_linreg)
@@ -364,7 +364,7 @@ theta <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
         random_index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
         xi <span style="color: #666666">=</span> X[random_index:random_index<span style="color: #666666">+1</span>]
         yi <span style="color: #666666">=</span> y[random_index:random_index<span style="color: #666666">+1</span>]
-        gradients <span style="color: #666666">=</span> (<span style="color: #666666">2.0/</span>m) <span style="color: #666666">*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
+        gradients <span style="color: #666666">=</span> <span style="color: #666666">2.0*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
         eta <span style="color: #666666">=</span> learning_schedule(epoch<span style="color: #666666">*</span>m<span style="color: #666666">+</span>i)
         theta <span style="color: #666666">=</span> theta <span style="color: #666666">-</span> eta<span style="color: #666666">*</span>gradients
 <span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;theta from own sdg&quot;</span>)
diff --git a/doc/pub/week40/html/week40-reveal.html b/doc/pub/week40/html/week40-reveal.html
index 0cee2bee7..b271cd4fd 100644
--- a/doc/pub/week40/html/week40-reveal.html
+++ b/doc/pub/week40/html/week40-reveal.html
@@ -436,11 +436,11 @@ We note that we have defined several hyperparameters. These are now the number o
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> SGDRegressor
 
-n = <span style="color: #B452CD">100</span>
+n = <span style="color: #B452CD">1000</span>
 x = <span style="color: #B452CD">2</span>*np.random.rand(n,<span style="color: #B452CD">1</span>)
 y = <span style="color: #B452CD">4</span>+<span style="color: #B452CD">3</span>*x+np.random.randn(n,<span style="color: #B452CD">1</span>)
 
-X = np.c_[np.ones((m,<span style="color: #B452CD">1</span>)), x]
+X = np.c_[np.ones((n,<span style="color: #B452CD">1</span>)), x]
 theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Own inversion&quot;</span>)
 <span style="color: #658b00">print</span>(theta_linreg)
@@ -481,7 +481,7 @@ theta = np.random.randn(<span style="color: #B452CD">2</span>,<span style="color
         random_index = np.random.randint(m)
         xi = X[random_index:random_index+<span style="color: #B452CD">1</span>]
         yi = y[random_index:random_index+<span style="color: #B452CD">1</span>]
-        gradients = (<span style="color: #B452CD">2.0</span>/m) * xi.T @ ((xi @ theta)-yi)
+        gradients = <span style="color: #B452CD">2.0</span>* xi.T @ ((xi @ theta)-yi)
         eta = learning_schedule(epoch*m+i)
         theta = theta - eta*gradients
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;theta from own sdg&quot;</span>)
diff --git a/doc/pub/week40/html/week40-solarized.html b/doc/pub/week40/html/week40-solarized.html
index 2ad884dd5..917133f75 100644
--- a/doc/pub/week40/html/week40-solarized.html
+++ b/doc/pub/week40/html/week40-solarized.html
@@ -525,11 +525,11 @@ We note that we have defined several hyperparameters. These are now the number o
 <span style="color: #8B008B; font-weight: bold">import</span> <span style="color: #008b45; text-decoration: underline">matplotlib.pyplot</span> <span style="color: #8B008B; font-weight: bold">as</span> <span style="color: #008b45; text-decoration: underline">plt</span>
 <span style="color: #8B008B; font-weight: bold">from</span> <span style="color: #008b45; text-decoration: underline">sklearn.linear_model</span> <span style="color: #8B008B; font-weight: bold">import</span> SGDRegressor
 
-n = <span style="color: #B452CD">100</span>
+n = <span style="color: #B452CD">1000</span>
 x = <span style="color: #B452CD">2</span>*np.random.rand(n,<span style="color: #B452CD">1</span>)
 y = <span style="color: #B452CD">4</span>+<span style="color: #B452CD">3</span>*x+np.random.randn(n,<span style="color: #B452CD">1</span>)
 
-X = np.c_[np.ones((m,<span style="color: #B452CD">1</span>)), x]
+X = np.c_[np.ones((n,<span style="color: #B452CD">1</span>)), x]
 theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;Own inversion&quot;</span>)
 <span style="color: #658b00">print</span>(theta_linreg)
@@ -570,7 +570,7 @@ theta = np.random.randn(<span style="color: #B452CD">2</span>,<span style="color
         random_index = np.random.randint(m)
         xi = X[random_index:random_index+<span style="color: #B452CD">1</span>]
         yi = y[random_index:random_index+<span style="color: #B452CD">1</span>]
-        gradients = (<span style="color: #B452CD">2.0</span>/m) * xi.T @ ((xi @ theta)-yi)
+        gradients = <span style="color: #B452CD">2.0</span>* xi.T @ ((xi @ theta)-yi)
         eta = learning_schedule(epoch*m+i)
         theta = theta - eta*gradients
 <span style="color: #658b00">print</span>(<span style="color: #CD5555">&quot;theta from own sdg&quot;</span>)
diff --git a/doc/pub/week40/html/week40.html b/doc/pub/week40/html/week40.html
index 0bb83f2fb..e9ed59b8a 100644
--- a/doc/pub/week40/html/week40.html
+++ b/doc/pub/week40/html/week40.html
@@ -530,11 +530,11 @@ We note that we have defined several hyperparameters. These are now the number o
 <span style="color: #008000; font-weight: bold">import</span> <span style="color: #0000FF; font-weight: bold">matplotlib.pyplot</span> <span style="color: #008000; font-weight: bold">as</span> <span style="color: #0000FF; font-weight: bold">plt</span>
 <span style="color: #008000; font-weight: bold">from</span> <span style="color: #0000FF; font-weight: bold">sklearn.linear_model</span> <span style="color: #008000; font-weight: bold">import</span> SGDRegressor
 
-n <span style="color: #666666">=</span> <span style="color: #666666">100</span>
+n <span style="color: #666666">=</span> <span style="color: #666666">1000</span>
 x <span style="color: #666666">=</span> <span style="color: #666666">2*</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>rand(n,<span style="color: #666666">1</span>)
 y <span style="color: #666666">=</span> <span style="color: #666666">4+3*</span>x<span style="color: #666666">+</span>np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randn(n,<span style="color: #666666">1</span>)
 
-X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c_[np<span style="color: #666666">.</span>ones((m,<span style="color: #666666">1</span>)), x]
+X <span style="color: #666666">=</span> np<span style="color: #666666">.</span>c_[np<span style="color: #666666">.</span>ones((n,<span style="color: #666666">1</span>)), x]
 theta_linreg <span style="color: #666666">=</span> np<span style="color: #666666">.</span>linalg<span style="color: #666666">.</span>inv(X<span style="color: #666666">.</span>T <span style="color: #666666">@</span> X) <span style="color: #666666">@</span> (X<span style="color: #666666">.</span>T <span style="color: #666666">@</span> y)
 <span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;Own inversion&quot;</span>)
 <span style="color: #008000">print</span>(theta_linreg)
@@ -575,7 +575,7 @@ theta <span style="color: #666666">=</span> np<span style="color: #666666">.</sp
         random_index <span style="color: #666666">=</span> np<span style="color: #666666">.</span>random<span style="color: #666666">.</span>randint(m)
         xi <span style="color: #666666">=</span> X[random_index:random_index<span style="color: #666666">+1</span>]
         yi <span style="color: #666666">=</span> y[random_index:random_index<span style="color: #666666">+1</span>]
-        gradients <span style="color: #666666">=</span> (<span style="color: #666666">2.0/</span>m) <span style="color: #666666">*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
+        gradients <span style="color: #666666">=</span> <span style="color: #666666">2.0*</span> xi<span style="color: #666666">.</span>T <span style="color: #666666">@</span> ((xi <span style="color: #666666">@</span> theta)<span style="color: #666666">-</span>yi)
         eta <span style="color: #666666">=</span> learning_schedule(epoch<span style="color: #666666">*</span>m<span style="color: #666666">+</span>i)
         theta <span style="color: #666666">=</span> theta <span style="color: #666666">-</span> eta<span style="color: #666666">*</span>gradients
 <span style="color: #008000">print</span>(<span style="color: #BA2121">&quot;theta from own sdg&quot;</span>)
diff --git a/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz b/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz
index e1c7ba345..912b3cb08 100644
Binary files a/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz and b/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz differ
diff --git a/doc/pub/week40/ipynb/week40.ipynb b/doc/pub/week40/ipynb/week40.ipynb
index 4e1233143..60cfea892 100644
--- a/doc/pub/week40/ipynb/week40.ipynb
+++ b/doc/pub/week40/ipynb/week40.ipynb
@@ -311,11 +311,11 @@
     "import matplotlib.pyplot as plt\n",
     "from sklearn.linear_model import SGDRegressor\n",
     "\n",
-    "n = 100\n",
+    "n = 1000\n",
     "x = 2*np.random.rand(n,1)\n",
     "y = 4+3*x+np.random.randn(n,1)\n",
     "\n",
-    "X = np.c_[np.ones((m,1)), x]\n",
+    "X = np.c_[np.ones((n,1)), x]\n",
     "theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)\n",
     "print(\"Own inversion\")\n",
     "print(theta_linreg)\n",
@@ -356,7 +356,7 @@
     "        random_index = np.random.randint(m)\n",
     "        xi = X[random_index:random_index+1]\n",
     "        yi = y[random_index:random_index+1]\n",
-    "        gradients = (2.0/m) * xi.T @ ((xi @ theta)-yi)\n",
+    "        gradients = 2.0* xi.T @ ((xi @ theta)-yi)\n",
     "        eta = learning_schedule(epoch*m+i)\n",
     "        theta = theta - eta*gradients\n",
     "print(\"theta from own sdg\")\n",
diff --git a/doc/src/week40/week40.do.txt b/doc/src/week40/week40.do.txt
index cc16ff576..bbdf5f9af 100644
--- a/doc/src/week40/week40.do.txt
+++ b/doc/src/week40/week40.do.txt
@@ -236,11 +236,11 @@ import numpy as np
 import matplotlib.pyplot as plt
 from sklearn.linear_model import SGDRegressor
 
-n = 100
+n = 1000
 x = 2*np.random.rand(n,1)
 y = 4+3*x+np.random.randn(n,1)
 
-X = np.c_[np.ones((m,1)), x]
+X = np.c_[np.ones((n,1)), x]
 theta_linreg = np.linalg.inv(X.T @ X) @ (X.T @ y)
 print("Own inversion")
 print(theta_linreg)
@@ -281,7 +281,7 @@ for epoch in range(n_epochs):
         random_index = np.random.randint(m)
         xi = X[random_index:random_index+1]
         yi = y[random_index:random_index+1]
-        gradients = (2.0/m) * xi.T @ ((xi @ theta)-yi)
+        gradients = 2.0* xi.T @ ((xi @ theta)-yi)
         eta = learning_schedule(epoch*m+i)
         theta = theta - eta*gradients
 print("theta from own sdg")