Make push-pages

Author: mvdoc

README.html

@@ -184,12 +184,12 @@
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/shortclips/README.html">Shortclips tutorial</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/02_download_shortclips.html">Download the data set</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/03_compute_explainable_variance.html">Compute the explainable variance</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/04_understand_ridge_regression.html">Understand ridge regression and cross-validation</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/05_fit_wordnet_model.html">Fit a ridge model with wordnet features</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/04_understand_ridge_regression.html">Understand ridge regression and hyperparameter selection</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/05_fit_wordnet_model.html">Fit a voxelwise encoding model with WordNet features</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/06_visualize_hemodynamic_response.html">Visualize the hemodynamic response</a></li>
 <li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/07_extract_motion_energy.html">Extract motion-energy features from the stimuli</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/08_fit_motion_energy_model.html">Fit a ridge model with motion-energy features</a></li>
-<li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/09_fit_banded_ridge_model.html">Fit a banded ridge model with both wordnet and motion-energy features</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/08_fit_motion_energy_model.html">Fit a voxelwise encoding model with motion-energy features</a></li>
+<li class="toctree-l2"><a class="reference internal" href="notebooks/shortclips/09_fit_banded_ridge_model.html">Fit a voxelwise encoding model with both WordNet and motion-energy features</a></li>
 </ul>
 </details></li>
 <li class="toctree-l1 has-children"><a class="reference internal" href="notebooks/vim2/README.html">Vim-2 tutorial (optional)</a><details><summary><span class="toctree-toggle" role="presentation"><i class="fa-solid fa-chevron-down"></i></span></summary><ul>

_sources/notebooks/shortclips/01_setup_colab.ipynb

@@ -34,10 +34,9 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "## Install all required dependencies and download the data\n",
+        "## Install all required dependencies\n",
         "\n",
-        "Uncomment and run the following cell to download the required packages.\n",
-        "\n"
+        "Uncomment and run the following cell to download the required packages."
       ]
     },
     {
@@ -99,42 +98,6 @@
         "                       \"on your machine.\")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {},
-      "source": [
-        "Now run the following cell to download the data for the tutorials.\n",
-        "\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "collapsed": false
-      },
-      "outputs": [],
-      "source": [
-        "from voxelwise_tutorials.io import download_datalad\n",
-        "\n",
-        "DATAFILES = [\n",
-        "    \"features/motion_energy.hdf\",\n",
-        "    \"features/wordnet.hdf\",\n",
-        "    \"mappers/S01_mappers.hdf\",\n",
-        "    \"responses/S01_responses.hdf\",\n",
-        "]\n",
-        "\n",
-        "source = \"https://gin.g-node.org/gallantlab/shortclips\"\n",
-        "destination = \"/content/shortclips\"\n",
-        "\n",
-        "for datafile in DATAFILES:\n",
-        "    local_filename = download_datalad(\n",
-        "        datafile,\n",
-        "        destination=destination,\n",
-        "        source=source\n",
-        "    )"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {},

_sources/notebooks/shortclips/04_understand_ridge_regression.ipynb

@@ -4,13 +4,12 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n",
-        "# Understand ridge regression and cross-validation\n",
+        "# Understand ridge regression and hyperparameter selection\n",
         "\n",
         "In future examples, we will model the fMRI responses using a regularized linear\n",
         "regression known as *ridge regression*. This example explains why we use ridge\n",
         "regression, and how to use cross-validation to select the appropriate\n",
-        "regularization hyper-parameter.\n",
+        "regularization hyperparameter.\n",
         "\n",
         "Linear regression is a method to model the relation between some input\n",
         "variables $X \\in \\mathbb{R}^{(n \\times p)}$ (the features) and an output\n",

_sources/notebooks/shortclips/05_fit_wordnet_model.ipynb

@@ -4,8 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n",
-        "# Fit a ridge model with wordnet features\n",
+        "# Fit a voxelwise encoding model with WordNet features\n",
         "\n",
         "In this example, we model the fMRI responses with semantic \"wordnet\" features,\n",
         "manually annotated on each frame of the movie stimulus. The model is a\n",

_sources/notebooks/shortclips/08_fit_motion_energy_model.ipynb

@@ -4,8 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "\n",
-        "# Fit a ridge model with motion-energy features\n",
+        "# Fit a voxelwise encoding model with motion-energy features\n",
         "\n",
         "In this example, we model the fMRI responses with motion-energy features\n",
         "extracted from the movie stimulus. The model is a regularized linear regression\n",

_sources/notebooks/shortclips/09_fit_banded_ridge_model.ipynb

@@ -4,7 +4,7 @@
       "cell_type": "markdown",
       "metadata": {},
       "source": [
-        "# Fit a banded ridge model with both wordnet and motion-energy features\n",
+        "# Fit a voxelwise encoding model with both WordNet and motion-energy features\n",
         "\n",
         "In this example, we model the fMRI responses with a *banded ridge regression* \n",
         "with two different feature spaces: motion energy and wordnet categories.\n",