Staging

tutorials/W1D3_ComparingArtificialAndBiologicalNetworks/W1D3_Tutorial4.ipynb

@@ -17,17 +17,17 @@
     "execution": {}
    },
    "source": [
-    "# Tutorial 4: Representational geometry & noise\n",
+    "# (Bonus) Tutorial 4: Representational geometry & noise\n",
     "\n",
     "**Week 1, Day 3: Comparing Artificial And Biological Networks**\n",
     "\n",
     "**By Neuromatch Academy**\n",
     "\n",
     "__Content creators:__ Wenxuan Guo, Heiko Schütt\n",
     "\n",
-    "__Content reviewers:__ Alish Dipani, Samuele Bolotta, Yizhou Chen, RyeongKyung Yoon, Ruiyi Zhang, Lily Chamakura, Hlib Solodzhuk\n",
+    "__Content reviewers:__ Alish Dipani, Samuele Bolotta, Yizhou Chen, RyeongKyung Yoon, Ruiyi Zhang, Lily Chamakura, Hlib Solodzhuk, Alex Murphy\n",
     "\n",
-    "__Production editors:__ Konstantine Tsafatinos, Ella Batty, Spiros Chavlis, Samuele Bolotta, Hlib Solodzhuk, Patrick Mineault\n",
+    "__Production editors:__ Konstantine Tsafatinos, Ella Batty, Spiros Chavlis, Samuele Bolotta, Hlib Solodzhuk, Patrick Mineault, Alex Murphy\n",
     "\n",
     "Acknowledgments: the tutorial outline was written by Heiko Schütt. The content was greatly improved by discussions with Heiko, Hlib, and Alish, and the insightful illustrations presented in the paper by Walther et al. (2016)\n"
    ]
@@ -61,7 +61,7 @@
     "\n",
     "5. Using random projections to estimate distances. This section introduces the Johnson–Lindenstrauss Lemma, which states that random projections maintain the integrity of distance estimates in a lower-dimensional space. This concept is crucial for reducing dimensionality while preserving the relational structure of the data.\n",
     "\n",
-    "We will adhere to the notational conventions established by [Walther et al. (2016)](https://pubmed.ncbi.nlm.nih.gov/26707889/) for all discussed distance measures. "
+    "We will adhere to the notational conventions established by [Walther et al. (2016)](https://pubmed.ncbi.nlm.nih.gov/26707889/) for all discussed distance measures."
    ]
   },
   {
@@ -644,6 +644,72 @@
     "display(tabs)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "b64eaea5",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "The video below is additional information in more detail which was previously part of the introductory video for this course day. It provides some useful further information on the technical details mentioned during these tutorials. Please feel free to check it out and use it as a resource if you want to learn more or if you want to get a deeper understanding on some of the important details."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "200235dc",
+   "metadata": {
+    "cellView": "form",
+    "execution": {}
+   },
+   "outputs": [],
+   "source": [
+    "# @title Video 2 (BONUS): Extended Intro Video\n",
+    "\n",
+    "from ipywidgets import widgets\n",
+    "from IPython.display import YouTubeVideo\n",
+    "from IPython.display import IFrame\n",
+    "from IPython.display import display\n",
+    "\n",
+    "class PlayVideo(IFrame):\n",
+    "  def __init__(self, id, source, page=1, width=400, height=300, **kwargs):\n",
+    "    self.id = id\n",
+    "    if source == 'Bilibili':\n",
+    "      src = f'https://player.bilibili.com/player.html?bvid={id}&page={page}'\n",
+    "    elif source == 'Osf':\n",
+    "      src = f'https://mfr.ca-1.osf.io/render?url=https://osf.io/download/{id}/?direct%26mode=render'\n",
+    "    super(PlayVideo, self).__init__(src, width, height, **kwargs)\n",
+    "\n",
+    "def display_videos(video_ids, W=400, H=300, fs=1):\n",
+    "  tab_contents = []\n",
+    "  for i, video_id in enumerate(video_ids):\n",
+    "    out = widgets.Output()\n",
+    "    with out:\n",
+    "      if video_ids[i][0] == 'Youtube':\n",
+    "        video = YouTubeVideo(id=video_ids[i][1], width=W,\n",
+    "                             height=H, fs=fs, rel=0)\n",
+    "        print(f'Video available at https://youtube.com/watch?v={video.id}')\n",
+    "      else:\n",
+    "        video = PlayVideo(id=video_ids[i][1], source=video_ids[i][0], width=W,\n",
+    "                          height=H, fs=fs, autoplay=False)\n",
+    "        if video_ids[i][0] == 'Bilibili':\n",
+    "          print(f'Video available at https://www.bilibili.com/video/{video.id}')\n",
+    "        elif video_ids[i][0] == 'Osf':\n",
+    "          print(f'Video available at https://osf.io/{video.id}')\n",
+    "      display(video)\n",
+    "    tab_contents.append(out)\n",
+    "  return tab_contents\n",
+    "\n",
+    "#assert 1 == 0, \"Upload this video\"\n",
+    "video_ids = [('Youtube', 'm9srqTx5ci0'), ('Bilibili', 'BV1meVjz3Eeh')]\n",
+    "tab_contents = display_videos(video_ids, W=854, H=480)\n",
+    "tabs = widgets.Tab()\n",
+    "tabs.children = tab_contents\n",
+    "for i in range(len(tab_contents)):\n",
+    "  tabs.set_title(i, video_ids[i][0])\n",
+    "display(tabs)"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1503,6 +1569,20 @@
     "3. Cross-validated distance estimators (cross-validated Euclidean or Mahalanobis distance) can remove the positive bias introduced by noise.\n",
     "4. The Johnson–Lindenstrauss Lemma shows that random projections preserve the Euclidean distance with some distortions. Crucially, the distortion does not depend on the dimensionality of the original space."
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "40936ec4",
+   "metadata": {
+    "execution": {}
+   },
+   "source": [
+    "# The Big Picture\n",
+    "\n",
+    "The goal of this tutorial is to provide you with some mathematical tools for your NeuroAI researcher toolkit. What happens when you pull out the Euclidean metric from your toolkit and, while this has worked well in the past, suddenly in different scenarios it doesn't seem to perform so well. Aha, you spot the potential for correlated noise and you reach deeper into your toolkit and pull out the Mahalanobis metric, which implicitly undoes the correlated noise in the model. Perhaps you can't even tell if there is any correlated noise in your data and you try with both metrics, and Mahalanobis works well but Euclidean does not, that can be a hunch that leads you to confirm the presence of correlated noise. \n",
+    "\n",
+    "Sometimes you might be faced with dimensionalities that are just too high to practically deal with in your use case. Then, why not recall what you learned about how random projections can reduce the dimensionality of a feature space and be largely resistant to corrupting the applicability of distance metrics. These metrics also might work better in this lower dimensional space. If you apply this idea and need to justify it, just reach into your NeuroAI toolkit and pull out the Johnson-Lindenstrauss Lemma as your justification."
+   ]
   }
  ],
  "metadata": {
@@ -1533,7 +1613,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.19"
+   "version": "3.9.22"
   }
  },
  "nbformat": 4,