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diff --git a/_sources/docs/Lesson5c_segmentation_ViT.ipynb b/_sources/docs/Lesson5c_segmentation_ViT.ipynb
diff --git a/_sources/docs/Lesson7c_transfer_learning_hyperparam_opt.ipynb b/_sources/docs/Lesson7c_transfer_learning_hyperparam_opt.ipynb
@@ -2007,28 +2007,15 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 43,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "RUNoQNgtfNgt",
         "outputId": "0e036235-e291-4235-f437-33ee4235d904"
       },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "Predictions:\n",
-            " [0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0\n",
-            " 1 0 0]\n",
-            "Labels:\n",
-            " [1 0 6 6]\n",
-            "40 4\n"
-          ]
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Retrieve a batch of images from the test set\n",
         "image_batch, label_batch = test_dataset.as_numpy_iterator().next()\n",
@@ -2118,30 +2105,6 @@
         "Now we will instantiate the tuner algorithm, in this case one called [Hyperband](https://arxiv.org/pdf/1603.06560.pdf). Herein, we have to specify the objective to optimize for and the allowable number of epochs to train an experiment for (usually this is to a large value and safeguarded by an early stopping callback which we'll define next). Note that the `project_name` argument defines a folder that will hold all of the checkpoints and logs from the trials in this experiment. By default, if the search is rerun with this same directory, the search will resume from the state captured in those checkpoints. This can be changed by including `overwrite=True`."
       ]
     },
-    {
-      "cell_type": "code",
-      "execution_count": 45,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "XPmMOnPl1CtX",
-        "outputId": "fb77e591-ae86-4867-f70a-41edbe4a07c1"
-      },
-      "outputs": [
-        {
-          "name": "stdout",
-          "output_type": "stream",
-          "text": [
-            "/content/gdrive/My Drive/tf-eo-devseed-2-user_outputs_dir\n"
-          ]
-        }
-      ],
-      "source": [
-        "# Move to your workshop directory in order to write data\n",
-        "%cd $user_outputs_dir"
-      ]
-    },
     {
       "cell_type": "code",
       "execution_count": 46,

diff --git a/_sources/docs/index.md b/_sources/docs/index.md
@@ -1,7 +1,7 @@
 
 
 <!-- #region -->
-# Deep Learning with TensorFlow: <br> Tutorials for modeling LULC.
+# Deep Learning with TensorFlow: <br> Tutorials for Earth Science Applications.
 
 
 Authored by [**Development Seed**](https://developmentseed.org/) engineers [**Lillianne Thomas**](https://github.com/lillythomas) and [**Ryan Avery**](https://github.com/rbavery)

diff --git a/docs/Lesson1a_Intro_ML_NN_DL.html b/docs/Lesson1a_Intro_ML_NN_DL.html
@@ -66,7 +66,7 @@
     <link rel="index" title="Index" href="../genindex.html" />
     <link rel="search" title="Search" href="../search.html" />
     <link rel="next" title="Introduction to Google Colab" href="Lesson2a_Intro_to_Google_Colab.html" />
-    <link rel="prev" title="Deep Learning with TensorFlow: Tutorials for modeling LULC." href="index.html" />
+    <link rel="prev" title="Deep Learning with TensorFlow: Tutorials for Earth Science Applications." href="index.html" />
   <meta name="viewport" content="width=device-width, initial-scale=1"/>
   <meta name="docsearch:language" content="en"/>
   </head>
@@ -790,7 +790,7 @@ <h2>References<a class="headerlink" href="#references" title="Permalink to this
       <i class="fa-solid fa-angle-left"></i>
       <div class="prev-next-info">
         <p class="prev-next-subtitle">previous</p>
-        <p class="prev-next-title">Deep Learning with TensorFlow: <br> Tutorials for modeling LULC.</p>
+        <p class="prev-next-title">Deep Learning with TensorFlow: <br> Tutorials for Earth Science Applications.</p>
       </div>
     </a>
     <a class="right-next"

diff --git a/docs/Lesson5c_segmentation_ViT.html b/docs/Lesson5c_segmentation_ViT.html
diff --git a/docs/Lesson7c_transfer_learning_hyperparam_opt.html b/docs/Lesson7c_transfer_learning_hyperparam_opt.html
@@ -1708,16 +1708,6 @@ <h3>Evaluation and prediction<a class="headerlink" href="#evaluation-and-predict
 </pre></div>
 </div>
 </div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Predictions:
- [0 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0
- 1 0 0]
-Labels:
- [1 0 6 6]
-40 4
-</pre></div>
-</div>
-</div>
 </div>
 </section>
 </section>
@@ -1766,19 +1756,6 @@ <h2>Hyperparameter Tuning<a class="headerlink" href="#id2" title="Permalink to t
 <p>Now we will instantiate the tuner algorithm, in this case one called <a class="reference external" href="https://arxiv.org/pdf/1603.06560.pdf">Hyperband</a>. Herein, we have to specify the objective to optimize for and the allowable number of epochs to train an experiment for (usually this is to a large value and safeguarded by an early stopping callback which we’ll define next). Note that the <code class="docutils literal notranslate"><span class="pre">project_name</span></code> argument defines a folder that will hold all of the checkpoints and logs from the trials in this experiment. By default, if the search is rerun with this same directory, the search will resume from the state captured in those checkpoints. This can be changed by including <code class="docutils literal notranslate"><span class="pre">overwrite=True</span></code>.</p>
 <div class="cell docutils container">
 <div class="cell_input docutils container">
-<div class="highlight-none notranslate"><div class="highlight"><pre><span></span># Move to your workshop directory in order to write data
-%cd $user_outputs_dir
-</pre></div>
-</div>
-</div>
-<div class="cell_output docutils container">
-<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>/content/gdrive/My Drive/tf-eo-devseed-2-user_outputs_dir
-</pre></div>
-</div>
-</div>
-</div>
-<div class="cell docutils container">
-<div class="cell_input docutils container">
 <div class="highlight-none notranslate"><div class="highlight"><pre><span></span>tuner = kt.Hyperband(model_builder,
                      objective=&#39;accuracy&#39;,
                      max_epochs=4,

diff --git a/docs/index.html b/docs/index.html
@@ -9,7 +9,7 @@
     <meta charset="utf-8" />
     <meta name="viewport" content="width=device-width, initial-scale=1.0" /><meta name="generator" content="Docutils 0.18.1: http://docutils.sourceforge.net/" />
 
-    <title>Deep Learning with TensorFlow: Tutorials for modeling LULC. &#8212; Deep learning with TensorFlow</title>
+    <title>Deep Learning with TensorFlow: Tutorials for Earth Science Applications. &#8212; Deep learning with TensorFlow</title>
 
 
 
@@ -385,7 +385,7 @@
 
 
 <div id="jb-print-docs-body" class="onlyprint">
-    <h1>Deep Learning with TensorFlow: <br> Tutorials for modeling LULC.</h1>
+    <h1>Deep Learning with TensorFlow: <br> Tutorials for Earth Science Applications.</h1>
     <!-- Table of contents -->
     <div id="print-main-content">
         <div id="jb-print-toc">
@@ -400,8 +400,8 @@ <h1>Deep Learning with TensorFlow: <br> Tutorials for modeling LULC.</h1>
                 <article class="bd-article" role="main">
 
   <!-- #region -->
-<section class="tex2jax_ignore mathjax_ignore" id="deep-learning-with-tensorflow-br-tutorials-for-modeling-lulc">
-<h1>Deep Learning with TensorFlow: <br> Tutorials for modeling LULC.<a class="headerlink" href="#deep-learning-with-tensorflow-br-tutorials-for-modeling-lulc" title="Permalink to this heading">#</a></h1>
+<section class="tex2jax_ignore mathjax_ignore" id="deep-learning-with-tensorflow-br-tutorials-for-earth-science-applications">
+<h1>Deep Learning with TensorFlow: <br> Tutorials for Earth Science Applications.<a class="headerlink" href="#deep-learning-with-tensorflow-br-tutorials-for-earth-science-applications" title="Permalink to this heading">#</a></h1>
 <p>Authored by <a class="reference external" href="https://developmentseed.org/"><strong>Development Seed</strong></a> engineers <a class="reference external" href="https://github.com/lillythomas"><strong>Lillianne Thomas</strong></a> and <a class="reference external" href="https://github.com/rbavery"><strong>Ryan Avery</strong></a></p>
 <!-- #endregion -->
 <p>These materials are designed to provide TensorFlow expertise via tutorials and science support, vis a vis suggestions for acquisition and processing of data inputs, training, testing, and evaluation of TensorFlow models as well as different TensorFlow / deep learning techniques demoed in Colab notebooks using real data.</p>

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