correcting issues (#117)

* add a note for video 5 * correcting a comment * correct mismatch between code and instructions * Process tutorial notebooks --------- Co-authored-by: GitHub Action <action@github.com>
NeuromatchAcademy · Jul 9, 2023 · b7f2432 · b7f2432
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diff --git a/tutorials/W0D1_PythonWorkshop1/W0D1_Tutorial1.ipynb b/tutorials/W0D1_PythonWorkshop1/W0D1_Tutorial1.ipynb
@@ -2009,10 +2009,10 @@
     "  # Plot sample mean using alpha=0.8 and'C0.' for blue\n",
     "  plt.plot(t, v_mean, 'C0.', alpha=0.8, markersize=10)\n",
     "\n",
-    "  # Plot mean + standard deviation with alpha=0.8 and argument 'C7'\n",
+    "  # Plot mean + standard deviation with alpha=0.8 and argument 'C7.'\n",
     "  plt.plot(...)\n",
     "\n",
-    "  # Plot mean - standard deviation with alpha=0.8 and argument 'C7'\n",
+    "  # Plot mean - standard deviation with alpha=0.8 and argument 'C7.'\n",
     "  plt.plot(...)\n",
     "\n",
     "\n",
@@ -2081,10 +2081,10 @@
     "    # Plot sample mean using alpha=0.8 and'C0.' for blue\n",
     "    plt.plot(t, v_mean, 'C0.', alpha=0.8, markersize=10)\n",
     "\n",
-    "    # Plot mean + standard deviation with alpha=0.8 and argument 'C7'\n",
+    "    # Plot mean + standard deviation with alpha=0.8 and argument 'C7.'\n",
     "    plt.plot(t, v_mean + v_std, 'C7.', alpha=0.8)\n",
     "\n",
-    "    # Plot mean - standard deviation with alpha=0.8 and argument 'C7'\n",
+    "    # Plot mean - standard deviation with alpha=0.8 and argument 'C7.'\n",
     "    plt.plot(t, v_mean - v_std, 'C7.', alpha=0.8)\n",
     "\n",
     "\n",

diff --git a/tutorials/W0D1_PythonWorkshop1/instructor/W0D1_Tutorial1.ipynb b/tutorials/W0D1_PythonWorkshop1/instructor/W0D1_Tutorial1.ipynb
@@ -2027,10 +2027,10 @@
     "  # Plot sample mean using alpha=0.8 and'C0.' for blue\n",
     "  plt.plot(t, v_mean, 'C0.', alpha=0.8, markersize=10)\n",
     "\n",
-    "  # Plot mean + standard deviation with alpha=0.8 and argument 'C7'\n",
+    "  # Plot mean + standard deviation with alpha=0.8 and argument 'C7.'\n",
     "  plt.plot(...)\n",
     "\n",
-    "  # Plot mean - standard deviation with alpha=0.8 and argument 'C7'\n",
+    "  # Plot mean - standard deviation with alpha=0.8 and argument 'C7.'\n",
     "  plt.plot(...)\n",
     "\n",
     "\n",
@@ -2101,10 +2101,10 @@
     "    # Plot sample mean using alpha=0.8 and'C0.' for blue\n",
     "    plt.plot(t, v_mean, 'C0.', alpha=0.8, markersize=10)\n",
     "\n",
-    "    # Plot mean + standard deviation with alpha=0.8 and argument 'C7'\n",
+    "    # Plot mean + standard deviation with alpha=0.8 and argument 'C7.'\n",
     "    plt.plot(t, v_mean + v_std, 'C7.', alpha=0.8)\n",
     "\n",
-    "    # Plot mean - standard deviation with alpha=0.8 and argument 'C7'\n",
+    "    # Plot mean - standard deviation with alpha=0.8 and argument 'C7.'\n",
     "    plt.plot(t, v_mean - v_std, 'C7.', alpha=0.8)\n",
     "\n",
     "\n",

diff --git a/...tions/W0D1_Tutorial1_Solution_4441778c.py → ...tions/W0D1_Tutorial1_Solution_c05a930d.py b/...tions/W0D1_Tutorial1_Solution_4441778c.py → ...tions/W0D1_Tutorial1_Solution_c05a930d.py
@@ -50,10 +50,10 @@
     # Plot sample mean using alpha=0.8 and'C0.' for blue
     plt.plot(t, v_mean, 'C0.', alpha=0.8, markersize=10)
 
-    # Plot mean + standard deviation with alpha=0.8 and argument 'C7'
+    # Plot mean + standard deviation with alpha=0.8 and argument 'C7.'
     plt.plot(t, v_mean + v_std, 'C7.', alpha=0.8)
 
-    # Plot mean - standard deviation with alpha=0.8 and argument 'C7'
+    # Plot mean - standard deviation with alpha=0.8 and argument 'C7.'
     plt.plot(t, v_mean - v_std, 'C7.', alpha=0.8)
 
 

diff --git a/...ic/W0D1_Tutorial1_Solution_4441778c_0.png → ...ic/W0D1_Tutorial1_Solution_c05a930d_0.png b/...ic/W0D1_Tutorial1_Solution_4441778c_0.png → ...ic/W0D1_Tutorial1_Solution_c05a930d_0.png
diff --git a/tutorials/W0D1_PythonWorkshop1/student/W0D1_Tutorial1.ipynb b/tutorials/W0D1_PythonWorkshop1/student/W0D1_Tutorial1.ipynb
@@ -1801,10 +1801,10 @@
     "  # Plot sample mean using alpha=0.8 and'C0.' for blue\n",
     "  plt.plot(t, v_mean, 'C0.', alpha=0.8, markersize=10)\n",
     "\n",
-    "  # Plot mean + standard deviation with alpha=0.8 and argument 'C7'\n",
+    "  # Plot mean + standard deviation with alpha=0.8 and argument 'C7.'\n",
     "  plt.plot(...)\n",
     "\n",
-    "  # Plot mean - standard deviation with alpha=0.8 and argument 'C7'\n",
+    "  # Plot mean - standard deviation with alpha=0.8 and argument 'C7.'\n",
     "  plt.plot(...)\n",
     "\n",
     "\n",
@@ -1819,11 +1819,11 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/NeuromatchAcademy/precourse/tree/main/tutorials/W0D1_PythonWorkshop1/solutions/W0D1_Tutorial1_Solution_4441778c.py)\n",
+    "[*Click for solution*](https://github.com/NeuromatchAcademy/precourse/tree/main/tutorials/W0D1_PythonWorkshop1/solutions/W0D1_Tutorial1_Solution_c05a930d.py)\n",
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=778.0 height=577.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D1_PythonWorkshop1/static/W0D1_Tutorial1_Solution_4441778c_0.png>\n",
+    "<img alt='Solution hint' align='left' width=778.0 height=577.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D1_PythonWorkshop1/static/W0D1_Tutorial1_Solution_c05a930d_0.png>\n",
     "\n"
    ]
   },

diff --git a/tutorials/W0D2_PythonWorkshop2/W0D2_Tutorial1.ipynb b/tutorials/W0D2_PythonWorkshop2/W0D2_Tutorial1.ipynb
@@ -424,7 +424,7 @@
     "n = 10000\n",
     "v_n = el * np.ones([n, step_end])\n",
     "i = i_mean * (1 + 0.1 * (t_max / dt)**(0.5) * (2 * np.random.random([n, step_end]) - 1))\n",
-    "nbins = 32\n",
+    "nbins = 50\n",
     "\n",
     "# Loop over time steps\n",
     "for step, t in enumerate(t_range):\n",
@@ -471,7 +471,7 @@
     "n = 10000\n",
     "v_n = el * np.ones([n, step_end])\n",
     "i = i_mean * (1 + 0.1 * (t_max / dt)**(0.5) * (2 * np.random.random([n, step_end]) - 1))\n",
-    "nbins = 32\n",
+    "nbins = 50\n",
     "\n",
     "# Loop over time steps\n",
     "for step, t in enumerate(t_range):\n",
@@ -490,14 +490,12 @@
     "  plt.xlabel('$V_m$ (V)')\n",
     "\n",
     "  # Plot a histogram at t_max/10 (add labels and parameters histtype='stepfilled' and linewidth=0)\n",
-    "  plt.hist(v_n[:,int(step_end / 10)], nbins,\n",
-    "            histtype='stepfilled', linewidth=0,\n",
-    "            label = 't='+ str(t_max / 10) + 's')\n",
+    "  plt.hist(v_n[:,int(step_end / 10)], nbins, histtype='stepfilled',\n",
+    "           linewidth=0, label=f't={t_max / 10} s')\n",
     "\n",
     "  # Plot a histogram at t_max (add labels and parameters histtype='stepfilled' and linewidth=0)\n",
-    "  plt.hist(v_n[:, -1], nbins,\n",
-    "            histtype='stepfilled', linewidth=0,\n",
-    "            label = 't='+ str(t_max) + 's')\n",
+    "  plt.hist(v_n[:, -1], nbins, histtype='stepfilled',\n",
+    "           linewidth=0, label=f't={t_max} s')\n",
     "  # Add legend\n",
     "  plt.legend()\n",
     "  plt.show()"

diff --git a/tutorials/W0D2_PythonWorkshop2/instructor/W0D2_Tutorial1.ipynb b/tutorials/W0D2_PythonWorkshop2/instructor/W0D2_Tutorial1.ipynb
@@ -424,7 +424,7 @@
     "n = 10000\n",
     "v_n = el * np.ones([n, step_end])\n",
     "i = i_mean * (1 + 0.1 * (t_max / dt)**(0.5) * (2 * np.random.random([n, step_end]) - 1))\n",
-    "nbins = 32\n",
+    "nbins = 50\n",
     "\n",
     "# Loop over time steps\n",
     "for step, t in enumerate(t_range):\n",
@@ -473,7 +473,7 @@
     "n = 10000\n",
     "v_n = el * np.ones([n, step_end])\n",
     "i = i_mean * (1 + 0.1 * (t_max / dt)**(0.5) * (2 * np.random.random([n, step_end]) - 1))\n",
-    "nbins = 32\n",
+    "nbins = 50\n",
     "\n",
     "# Loop over time steps\n",
     "for step, t in enumerate(t_range):\n",
@@ -492,14 +492,12 @@
     "  plt.xlabel('$V_m$ (V)')\n",
     "\n",
     "  # Plot a histogram at t_max/10 (add labels and parameters histtype='stepfilled' and linewidth=0)\n",
-    "  plt.hist(v_n[:,int(step_end / 10)], nbins,\n",
-    "            histtype='stepfilled', linewidth=0,\n",
-    "            label = 't='+ str(t_max / 10) + 's')\n",
+    "  plt.hist(v_n[:,int(step_end / 10)], nbins, histtype='stepfilled',\n",
+    "           linewidth=0, label=f't={t_max / 10} s')\n",
     "\n",
     "  # Plot a histogram at t_max (add labels and parameters histtype='stepfilled' and linewidth=0)\n",
-    "  plt.hist(v_n[:, -1], nbins,\n",
-    "            histtype='stepfilled', linewidth=0,\n",
-    "            label = 't='+ str(t_max) + 's')\n",
+    "  plt.hist(v_n[:, -1], nbins, histtype='stepfilled',\n",
+    "           linewidth=0, label=f't={t_max} s')\n",
     "  # Add legend\n",
     "  plt.legend()\n",
     "  plt.show()"

diff --git a/...tions/W0D2_Tutorial1_Solution_04b855a3.py → ...tions/W0D2_Tutorial1_Solution_c135b57a.py b/...tions/W0D2_Tutorial1_Solution_04b855a3.py → ...tions/W0D2_Tutorial1_Solution_c135b57a.py
@@ -8,7 +8,7 @@
 n = 10000
 v_n = el * np.ones([n, step_end])
 i = i_mean * (1 + 0.1 * (t_max / dt)**(0.5) * (2 * np.random.random([n, step_end]) - 1))
-nbins = 32
+nbins = 50
 
 # Loop over time steps
 for step, t in enumerate(t_range):
@@ -27,14 +27,12 @@
   plt.xlabel('$V_m$ (V)')
 
   # Plot a histogram at t_max/10 (add labels and parameters histtype='stepfilled' and linewidth=0)
-  plt.hist(v_n[:,int(step_end / 10)], nbins,
-            histtype='stepfilled', linewidth=0,
-            label = 't='+ str(t_max / 10) + 's')
+  plt.hist(v_n[:,int(step_end / 10)], nbins, histtype='stepfilled',
+           linewidth=0, label=f't={t_max / 10} s')
 
   # Plot a histogram at t_max (add labels and parameters histtype='stepfilled' and linewidth=0)
-  plt.hist(v_n[:, -1], nbins,
-            histtype='stepfilled', linewidth=0,
-            label = 't='+ str(t_max) + 's')
+  plt.hist(v_n[:, -1], nbins, histtype='stepfilled',
+           linewidth=0, label=f't={t_max} s')
   # Add legend
   plt.legend()
   plt.show()
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_04b855a3_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_04b855a3_0.png
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_130ba4a4_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_130ba4a4_0.png
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_3973c4c4_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_3973c4c4_0.png
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_a22fdac7_1.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_a22fdac7_1.png
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_0.png
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_1.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_1.png
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c135b57a_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c135b57a_0.png
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_0.png
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_1.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_1.png
diff --git a/tutorials/W0D2_PythonWorkshop2/student/W0D2_Tutorial1.ipynb b/tutorials/W0D2_PythonWorkshop2/student/W0D2_Tutorial1.ipynb
@@ -424,7 +424,7 @@
     "n = 10000\n",
     "v_n = el * np.ones([n, step_end])\n",
     "i = i_mean * (1 + 0.1 * (t_max / dt)**(0.5) * (2 * np.random.random([n, step_end]) - 1))\n",
-    "nbins = 32\n",
+    "nbins = 50\n",
     "\n",
     "# Loop over time steps\n",
     "for step, t in enumerate(t_range):\n",
@@ -459,11 +459,11 @@
     "execution": {}
    },
    "source": [
-    "[*Click for solution*](https://github.com/NeuromatchAcademy/precourse/tree/main/tutorials/W0D2_PythonWorkshop2/solutions/W0D2_Tutorial1_Solution_04b855a3.py)\n",
+    "[*Click for solution*](https://github.com/NeuromatchAcademy/precourse/tree/main/tutorials/W0D2_PythonWorkshop2/solutions/W0D2_Tutorial1_Solution_c135b57a.py)\n",
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=778.0 height=577.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_04b855a3_0.png>\n",
+    "<img alt='Solution hint' align='left' width=777.0 height=577.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c135b57a_0.png>\n",
     "\n"
    ]
   },
@@ -1077,9 +1077,9 @@
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=777.0 height=577.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_0.png>\n",
+    "<img alt='Solution hint' align='left' width=776.0 height=578.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_0.png>\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=777.0 height=877.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_1.png>\n",
+    "<img alt='Solution hint' align='left' width=776.0 height=878.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_1.png>\n",
     "\n"
    ]
   },
@@ -1180,7 +1180,7 @@
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=777.0 height=877.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_3973c4c4_0.png>\n",
+    "<img alt='Solution hint' align='left' width=776.0 height=878.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_3973c4c4_0.png>\n",
     "\n"
    ]
   },
@@ -1444,7 +1444,7 @@
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=777.0 height=877.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_130ba4a4_0.png>\n",
+    "<img alt='Solution hint' align='left' width=776.0 height=878.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_130ba4a4_0.png>\n",
     "\n"
    ]
   },
@@ -1827,9 +1827,9 @@
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=777.0 height=378.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_0.png>\n",
+    "<img alt='Solution hint' align='left' width=776.0 height=372.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_0.png>\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=777.0 height=877.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_1.png>\n",
+    "<img alt='Solution hint' align='left' width=776.0 height=878.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_1.png>\n",
     "\n"
    ]
   },
@@ -2145,7 +2145,7 @@
     "\n",
     "*Example output:*\n",
     "\n",
-    "<img alt='Solution hint' align='left' width=777.0 height=878.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_a22fdac7_1.png>\n",
+    "<img alt='Solution hint' align='left' width=776.0 height=878.0 src=https://raw.githubusercontent.com/NeuromatchAcademy/precourse/main/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_a22fdac7_1.png>\n",
     "\n"
    ]
   },

diff --git a/tutorials/W0D5_Statistics/W0D5_Tutorial2.ipynb b/tutorials/W0D5_Statistics/W0D5_Tutorial2.ipynb
@@ -1740,6 +1740,15 @@
     "content_review(f\"{feedback_prefix}_Bayesian_inference_with_Gaussian_distribution_Video\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "**Note:** The Bayes rule in the video (0:14-3:05) contains a typo: the denominator should be $P(x)$ and not $P(x,\\theta)$. So, the formula should be $ P(\\theta | x) = \\frac{P(x|\\theta) P(\\theta)}{P(x)}$."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

diff --git a/tutorials/W0D5_Statistics/instructor/W0D5_Tutorial2.ipynb b/tutorials/W0D5_Statistics/instructor/W0D5_Tutorial2.ipynb
@@ -1748,6 +1748,15 @@
     "content_review(f\"{feedback_prefix}_Bayesian_inference_with_Gaussian_distribution_Video\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "**Note:** The Bayes rule in the video (0:14-3:05) contains a typo: the denominator should be $P(x)$ and not $P(x,\\theta)$. So, the formula should be $ P(\\theta | x) = \\frac{P(x|\\theta) P(\\theta)}{P(x)}$."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

diff --git a/tutorials/W0D5_Statistics/student/W0D5_Tutorial2.ipynb b/tutorials/W0D5_Statistics/student/W0D5_Tutorial2.ipynb
@@ -1607,6 +1607,15 @@
     "content_review(f\"{feedback_prefix}_Bayesian_inference_with_Gaussian_distribution_Video\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "**Note:** The Bayes rule in the video (0:14-3:05) contains a typo: the denominator should be $P(x)$ and not $P(x,\\theta)$. So, the formula should be $ P(\\theta | x) = \\frac{P(x|\\theta) P(\\theta)}{P(x)}$."
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {