diff --git a/tutorials/W0D1_PythonWorkshop1/W0D1_Tutorial1.ipynb b/tutorials/W0D1_PythonWorkshop1/W0D1_Tutorial1.ipynb
index e7fb5e8..0bcccf6 100644
--- 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
index f1b752d..1e61181 100644
--- 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/tutorials/W0D1_PythonWorkshop1/solutions/W0D1_Tutorial1_Solution_4441778c.py b/tutorials/W0D1_PythonWorkshop1/solutions/W0D1_Tutorial1_Solution_c05a930d.py
similarity index 99%
rename from tutorials/W0D1_PythonWorkshop1/solutions/W0D1_Tutorial1_Solution_4441778c.py
rename to tutorials/W0D1_PythonWorkshop1/solutions/W0D1_Tutorial1_Solution_c05a930d.py
index 61ec666..15bd364 100644
--- a/tutorials/W0D1_PythonWorkshop1/solutions/W0D1_Tutorial1_Solution_4441778c.py
+++ b/tutorials/W0D1_PythonWorkshop1/solutions/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/tutorials/W0D1_PythonWorkshop1/static/W0D1_Tutorial1_Solution_4441778c_0.png b/tutorials/W0D1_PythonWorkshop1/static/W0D1_Tutorial1_Solution_c05a930d_0.png
similarity index 100%
rename from tutorials/W0D1_PythonWorkshop1/static/W0D1_Tutorial1_Solution_4441778c_0.png
rename to tutorials/W0D1_PythonWorkshop1/static/W0D1_Tutorial1_Solution_c05a930d_0.png
diff --git a/tutorials/W0D1_PythonWorkshop1/student/W0D1_Tutorial1.ipynb b/tutorials/W0D1_PythonWorkshop1/student/W0D1_Tutorial1.ipynb
index eea2efa..afd9087 100644
--- 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",
- "
\n",
+ "
\n",
"\n"
]
},
diff --git a/tutorials/W0D2_PythonWorkshop2/W0D2_Tutorial1.ipynb b/tutorials/W0D2_PythonWorkshop2/W0D2_Tutorial1.ipynb
index b9151d1..1a4339d 100644
--- 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
index 41b3692..e410b9d 100644
--- 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/tutorials/W0D2_PythonWorkshop2/solutions/W0D2_Tutorial1_Solution_04b855a3.py b/tutorials/W0D2_PythonWorkshop2/solutions/W0D2_Tutorial1_Solution_c135b57a.py
similarity index 75%
rename from tutorials/W0D2_PythonWorkshop2/solutions/W0D2_Tutorial1_Solution_04b855a3.py
rename to tutorials/W0D2_PythonWorkshop2/solutions/W0D2_Tutorial1_Solution_c135b57a.py
index bd6785f..cdf622c 100644
--- a/tutorials/W0D2_PythonWorkshop2/solutions/W0D2_Tutorial1_Solution_04b855a3.py
+++ b/tutorials/W0D2_PythonWorkshop2/solutions/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()
\ No newline at end of file
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_04b855a3_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_04b855a3_0.png
deleted file mode 100644
index 3ef6e8b..0000000
Binary files a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_04b855a3_0.png and /dev/null differ
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_130ba4a4_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_130ba4a4_0.png
index 4ddcd95..1da2a57 100644
Binary files a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_130ba4a4_0.png and b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_130ba4a4_0.png differ
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_3973c4c4_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_3973c4c4_0.png
index b54aea4..a641480 100644
Binary files a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_3973c4c4_0.png and b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_3973c4c4_0.png differ
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_a22fdac7_1.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_a22fdac7_1.png
index 738b34b..bcd9ef7 100644
Binary files a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_a22fdac7_1.png and b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_a22fdac7_1.png differ
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_0.png
index b2dce30..c884718 100644
Binary files a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_0.png and b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_0.png differ
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_1.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_1.png
index f816f55..b725704 100644
Binary files a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_1.png and b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_bf9f75ab_1.png differ
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c135b57a_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c135b57a_0.png
new file mode 100644
index 0000000..b025861
Binary files /dev/null and b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c135b57a_0.png differ
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_0.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_0.png
index ee52005..ab26fec 100644
Binary files a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_0.png and b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_0.png differ
diff --git a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_1.png b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_1.png
index 2c77d8e..f0dff52 100644
Binary files a/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_1.png and b/tutorials/W0D2_PythonWorkshop2/static/W0D2_Tutorial1_Solution_c368c1ef_1.png differ
diff --git a/tutorials/W0D2_PythonWorkshop2/student/W0D2_Tutorial1.ipynb b/tutorials/W0D2_PythonWorkshop2/student/W0D2_Tutorial1.ipynb
index c7b4ffd..470e618 100644
--- 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",
- "
\n",
+ "
\n",
"\n"
]
},
@@ -1077,9 +1077,9 @@
"\n",
"*Example output:*\n",
"\n",
- "
\n",
+ "\n",
"\n",
- "
\n",
+ "\n",
"\n"
]
},
@@ -1180,7 +1180,7 @@
"\n",
"*Example output:*\n",
"\n",
- "
\n",
+ "\n",
"\n"
]
},
@@ -1444,7 +1444,7 @@
"\n",
"*Example output:*\n",
"\n",
- "
\n",
+ "\n",
"\n"
]
},
@@ -1827,9 +1827,9 @@
"\n",
"*Example output:*\n",
"\n",
- "
\n",
+ "\n",
"\n",
- "
\n",
+ "\n",
"\n"
]
},
@@ -2145,7 +2145,7 @@
"\n",
"*Example output:*\n",
"\n",
- "
\n",
+ "\n",
"\n"
]
},
diff --git a/tutorials/W0D5_Statistics/W0D5_Tutorial2.ipynb b/tutorials/W0D5_Statistics/W0D5_Tutorial2.ipynb
index e7303bd..79a339d 100644
--- 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
index 29eb751..9e6356b 100644
--- 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
index b12c40c..4272571 100644
--- 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": {