Merge pull request #358 from neuromatch/D8_links

W2D4

tutorials/W2D4_Macrolearning/W2D4_Tutorial1.ipynb

@@ -302,6 +302,19 @@
     "display(tabs)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# @title Submit your feedback\n",
+    "content_review(f\"{feedback_prefix}_distribution_shifts_video\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

tutorials/W2D4_Macrolearning/W2D4_Tutorial3.ipynb

@@ -1282,7 +1282,7 @@
     "execution": {}
    },
    "source": [
-    "You should be able to see that the meta-loss value is decreasing. Let us look at how the first layer weight values developed throughout the first 100 epochs. Here, we project this high-dimensional weight vector into a two-dimensional space using PCA. Play with the epoch value to see the (outer) evolution of the base weights (the left plot) and see how the (inner) task-specific weights change the total weight values in the given epoch (the right plot). For clarity of visualization, we use only the first ten tasks for each epoch in the inner weight visualizations."
+    "You should be able to see that the meta-loss value is 0.123 for this case. Let us look at how the first layer weight values developed throughout the first 100 epochs. Here, we project this high-dimensional weight vector into a two-dimensional space using PCA. Play with the epoch value to see the (outer) evolution of the base weights (the left plot) and see how the (inner) task-specific weights change the total weight values in the given epoch (the right plot). For clarity of visualization, we use only the first ten tasks for each epoch in the inner weight visualizations."
    ]
   },
   {
@@ -1643,7 +1643,7 @@
    },
    "outputs": [],
    "source": [
-    "# @title Make sure you execute this cell to observe the widget!\n",
+    "# @title Make sure you execute this cell to observe the plot!\n",
     "\n",
     "set_seed(42)\n",
     "\n",

tutorials/W2D4_Macrolearning/W2D4_Tutorial4.ipynb

@@ -582,7 +582,7 @@
    "outputs": [],
    "source": [
     "# @title Make sure you execute this cell to play the game!\n",
-    "#game()"
+    "game()"
    ]
   },
   {
@@ -996,8 +996,6 @@
    "source": [
     "set_seed(42)\n",
     "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
     "#define environment\n",
     "env = HarlowExperimentEnv()\n",
     "\n",
@@ -1564,7 +1562,7 @@
     "execution": {}
    },
    "source": [
-    "You should be able to see that the mean population score is `49.72`, and the best score is `100`. If you change `num_generations` to 800 in the previous code cell, the plot for the mean score in the population will roughly take the following form.\n",
+    " If you change `num_generations` to 800 in the previous code cell, the plot for the mean score in the population will roughly take the following form.\n",
     "\n",
     "![Picture which depicts the plot of mean scores per generation.](https://github.com/neuromatch/NeuroAI_Course/blob/main/tutorials/W2D4_Macrolearning/static/evolution.png?raw=true)\n",
     "\n",

tutorials/W2D4_Macrolearning/W2D4_Tutorial5.ipynb

@@ -91,7 +91,7 @@
    "source": [
     "# @title Install and import feedback gadget\n",
     "\n",
-    "!pip install numpy matplotlib scikit-learn ipywidgets jupyter-ui-poll torch --quiet\n",
+    "!pip install numpy matplotlib scikit-learn ipywidgets jupyter-ui-poll torch vibecheck --quiet\n",
     "\n",
     "from vibecheck import DatatopsContentReviewContainer\n",
     "def content_review(notebook_section: str):\n",
@@ -392,41 +392,6 @@
     "                    fid.write(r.content) # Write the downloaded content to a file"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# @title Set device (GPU or CPU)\n",
-    "\n",
-    "def set_device():\n",
-    "    \"\"\"\n",
-    "    Determines and sets the computational device for PyTorch operations based on the availability of a CUDA-capable GPU.\n",
-    "\n",
-    "    Outputs:\n",
-    "    - device (str): The device that PyTorch will use for computations ('cuda' or 'cpu'). This string can be directly used\n",
-    "    in PyTorch operations to specify the device.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "    if device != \"cuda\":\n",
-    "        print(\"GPU is not enabled in this notebook. \\n\"\n",
-    "              \"If you want to enable it, in the menu under `Runtime` -> \\n\"\n",
-    "              \"`Hardware accelerator.` and select `GPU` from the dropdown menu\")\n",
-    "    else:\n",
-    "        print(\"GPU is enabled in this notebook. \\n\"\n",
-    "              \"If you want to disable it, in the menu under `Runtime` -> \\n\"\n",
-    "              \"`Hardware accelerator.` and select `None` from the dropdown menu\")\n",
-    "\n",
-    "    return device\n",
-    "\n",
-    "device = set_device()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

tutorials/W2D4_Macrolearning/instructor/W2D4_Tutorial1.ipynb

@@ -302,6 +302,19 @@
     "display(tabs)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# @title Submit your feedback\n",
+    "content_review(f\"{feedback_prefix}_distribution_shifts_video\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

tutorials/W2D4_Macrolearning/instructor/W2D4_Tutorial3.ipynb

@@ -1286,7 +1286,7 @@
     "execution": {}
    },
    "source": [
-    "You should be able to see that the meta-loss value is decreasing. Let us look at how the first layer weight values developed throughout the first 100 epochs. Here, we project this high-dimensional weight vector into a two-dimensional space using PCA. Play with the epoch value to see the (outer) evolution of the base weights (the left plot) and see how the (inner) task-specific weights change the total weight values in the given epoch (the right plot). For clarity of visualization, we use only the first ten tasks for each epoch in the inner weight visualizations."
+    "You should be able to see that the meta-loss value is 0.123 for this case. Let us look at how the first layer weight values developed throughout the first 100 epochs. Here, we project this high-dimensional weight vector into a two-dimensional space using PCA. Play with the epoch value to see the (outer) evolution of the base weights (the left plot) and see how the (inner) task-specific weights change the total weight values in the given epoch (the right plot). For clarity of visualization, we use only the first ten tasks for each epoch in the inner weight visualizations."
    ]
   },
   {
@@ -1649,7 +1649,7 @@
    },
    "outputs": [],
    "source": [
-    "# @title Make sure you execute this cell to observe the widget!\n",
+    "# @title Make sure you execute this cell to observe the plot!\n",
     "\n",
     "set_seed(42)\n",
     "\n",

tutorials/W2D4_Macrolearning/instructor/W2D4_Tutorial4.ipynb

@@ -582,7 +582,7 @@
    "outputs": [],
    "source": [
     "# @title Make sure you execute this cell to play the game!\n",
-    "#game()"
+    "game()"
    ]
   },
   {
@@ -996,8 +996,6 @@
    "source": [
     "set_seed(42)\n",
     "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
     "#define environment\n",
     "env = HarlowExperimentEnv()\n",
     "\n",
@@ -1568,7 +1566,7 @@
     "execution": {}
    },
    "source": [
-    "You should be able to see that the mean population score is `49.72`, and the best score is `100`. If you change `num_generations` to 800 in the previous code cell, the plot for the mean score in the population will roughly take the following form.\n",
+    " If you change `num_generations` to 800 in the previous code cell, the plot for the mean score in the population will roughly take the following form.\n",
     "\n",
     "![Picture which depicts the plot of mean scores per generation.](https://github.com/neuromatch/NeuroAI_Course/blob/main/tutorials/W2D4_Macrolearning/static/evolution.png?raw=true)\n",
     "\n",

tutorials/W2D4_Macrolearning/instructor/W2D4_Tutorial5.ipynb

@@ -91,7 +91,7 @@
    "source": [
     "# @title Install and import feedback gadget\n",
     "\n",
-    "!pip install numpy matplotlib scikit-learn ipywidgets jupyter-ui-poll torch --quiet\n",
+    "!pip install numpy matplotlib scikit-learn ipywidgets jupyter-ui-poll torch vibecheck --quiet\n",
     "\n",
     "from vibecheck import DatatopsContentReviewContainer\n",
     "def content_review(notebook_section: str):\n",
@@ -392,41 +392,6 @@
     "                    fid.write(r.content) # Write the downloaded content to a file"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# @title Set device (GPU or CPU)\n",
-    "\n",
-    "def set_device():\n",
-    "    \"\"\"\n",
-    "    Determines and sets the computational device for PyTorch operations based on the availability of a CUDA-capable GPU.\n",
-    "\n",
-    "    Outputs:\n",
-    "    - device (str): The device that PyTorch will use for computations ('cuda' or 'cpu'). This string can be directly used\n",
-    "    in PyTorch operations to specify the device.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "    if device != \"cuda\":\n",
-    "        print(\"GPU is not enabled in this notebook. \\n\"\n",
-    "              \"If you want to enable it, in the menu under `Runtime` -> \\n\"\n",
-    "              \"`Hardware accelerator.` and select `GPU` from the dropdown menu\")\n",
-    "    else:\n",
-    "        print(\"GPU is enabled in this notebook. \\n\"\n",
-    "              \"If you want to disable it, in the menu under `Runtime` -> \\n\"\n",
-    "              \"`Hardware accelerator.` and select `None` from the dropdown menu\")\n",
-    "\n",
-    "    return device\n",
-    "\n",
-    "device = set_device()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,

tutorials/W2D4_Macrolearning/student/W2D4_Tutorial1.ipynb

@@ -302,6 +302,19 @@
     "display(tabs)"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# @title Submit your feedback\n",
+    "content_review(f\"{feedback_prefix}_distribution_shifts_video\")"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {

tutorials/W2D4_Macrolearning/student/W2D4_Tutorial3.ipynb

@@ -1143,7 +1143,7 @@
     "execution": {}
    },
    "source": [
-    "You should be able to see that the meta-loss value is decreasing. Let us look at how the first layer weight values developed throughout the first 100 epochs. Here, we project this high-dimensional weight vector into a two-dimensional space using PCA. Play with the epoch value to see the (outer) evolution of the base weights (the left plot) and see how the (inner) task-specific weights change the total weight values in the given epoch (the right plot). For clarity of visualization, we use only the first ten tasks for each epoch in the inner weight visualizations."
+    "You should be able to see that the meta-loss value is 0.123 for this case. Let us look at how the first layer weight values developed throughout the first 100 epochs. Here, we project this high-dimensional weight vector into a two-dimensional space using PCA. Play with the epoch value to see the (outer) evolution of the base weights (the left plot) and see how the (inner) task-specific weights change the total weight values in the given epoch (the right plot). For clarity of visualization, we use only the first ten tasks for each epoch in the inner weight visualizations."
    ]
   },
   {
@@ -1479,7 +1479,7 @@
    },
    "outputs": [],
    "source": [
-    "# @title Make sure you execute this cell to observe the widget!\n",
+    "# @title Make sure you execute this cell to observe the plot!\n",
     "\n",
     "set_seed(42)\n",
     "\n",

tutorials/W2D4_Macrolearning/student/W2D4_Tutorial4.ipynb

@@ -582,7 +582,7 @@
    "outputs": [],
    "source": [
     "# @title Make sure you execute this cell to play the game!\n",
-    "#game()"
+    "game()"
    ]
   },
   {
@@ -996,8 +996,6 @@
    "source": [
     "set_seed(42)\n",
     "\n",
-    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
-    "\n",
     "#define environment\n",
     "env = HarlowExperimentEnv()\n",
     "\n",
@@ -1510,7 +1508,7 @@
     "execution": {}
    },
    "source": [
-    "You should be able to see that the mean population score is `49.72`, and the best score is `100`. If you change `num_generations` to 800 in the previous code cell, the plot for the mean score in the population will roughly take the following form.\n",
+    " If you change `num_generations` to 800 in the previous code cell, the plot for the mean score in the population will roughly take the following form.\n",
     "\n",
     "![Picture which depicts the plot of mean scores per generation.](https://github.com/neuromatch/NeuroAI_Course/blob/main/tutorials/W2D4_Macrolearning/static/evolution.png?raw=true)\n",
     "\n",

tutorials/W2D4_Macrolearning/student/W2D4_Tutorial5.ipynb

@@ -91,7 +91,7 @@
    "source": [
     "# @title Install and import feedback gadget\n",
     "\n",
-    "!pip install numpy matplotlib scikit-learn ipywidgets jupyter-ui-poll torch --quiet\n",
+    "!pip install numpy matplotlib scikit-learn ipywidgets jupyter-ui-poll torch vibecheck --quiet\n",
     "\n",
     "from vibecheck import DatatopsContentReviewContainer\n",
     "def content_review(notebook_section: str):\n",
@@ -392,41 +392,6 @@
     "                    fid.write(r.content) # Write the downloaded content to a file"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "cellView": "form",
-    "execution": {}
-   },
-   "outputs": [],
-   "source": [
-    "# @title Set device (GPU or CPU)\n",
-    "\n",
-    "def set_device():\n",
-    "    \"\"\"\n",
-    "    Determines and sets the computational device for PyTorch operations based on the availability of a CUDA-capable GPU.\n",
-    "\n",
-    "    Outputs:\n",
-    "    - device (str): The device that PyTorch will use for computations ('cuda' or 'cpu'). This string can be directly used\n",
-    "    in PyTorch operations to specify the device.\n",
-    "    \"\"\"\n",
-    "\n",
-    "    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n",
-    "    if device != \"cuda\":\n",
-    "        print(\"GPU is not enabled in this notebook. \\n\"\n",
-    "              \"If you want to enable it, in the menu under `Runtime` -> \\n\"\n",
-    "              \"`Hardware accelerator.` and select `GPU` from the dropdown menu\")\n",
-    "    else:\n",
-    "        print(\"GPU is enabled in this notebook. \\n\"\n",
-    "              \"If you want to disable it, in the menu under `Runtime` -> \\n\"\n",
-    "              \"`Hardware accelerator.` and select `None` from the dropdown menu\")\n",
-    "\n",
-    "    return device\n",
-    "\n",
-    "device = set_device()"
-   ]
-  },
   {
    "cell_type": "code",
    "execution_count": null,