deploy: aeee660

Chapter1-GettingStarted/1.1_open_reproducible_science.html

@@ -568,8 +568,8 @@ <h1>1.1 Open Reproducible Science</h1>
 
   <section class="tex2jax_ignore mathjax_ignore" id="open-reproducible-science">
 <h1>1.1 Open Reproducible Science<a class="headerlink" href="#open-reproducible-science" title="Permalink to this headline">#</a></h1>
-<p><a class="reference external" href="https://docs.google.com/presentation/d/1SCrx65-Q_CB8JRMN81Uk-QniPvPE-wozRD7YDDQEapc/edit?usp=sharing"><img alt="Lecture Slides" src="img/Google_Slides_Logo.svg" /></a></p>
-<p><a class="reference external" href="https://youtu.be/WeZ2vJxBuTg?si=aG1IuBNhCQnb1ljv"><img alt="YouTube video" src="img/YouTube-Logo.wine.svg" /></a></p>
+<p><a class="reference external" href="https://docs.google.com/presentation/d/1SCrx65-Q_CB8JRMN81Uk-QniPvPE-wozRD7YDDQEapc/edit?usp=sharing"><img alt="Lecture Slides" src="../img/Google_Slides_Logo.svg" /></a></p>
+<p><a class="reference external" href="https://youtu.be/WeZ2vJxBuTg?si=aG1IuBNhCQnb1ljv"><img alt="YouTube video" src="../img/YouTube-Logo.wine.svg" /></a></p>
 </section>
 
     <script type="text/x-thebe-config">

Chapter2-DataManipulation/2.2_data_formats_rendered.html

@@ -794,7 +794,7 @@ <h3>1.1 rasterio to read GeoTIFF<a class="headerlink" href="#rasterio-to-read-ge
 <div class="cell_input docutils container">
 <div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># Dowload the data using wget.</span>
 <span class="n">fname</span> <span class="o">=</span> <span class="s1">&#39;HYP_50M_SR&#39;</span>
-<span class="n">wget</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s2">&quot;https://www.dropbox.com/s/r75ecms0bvyqaca/&quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span><span class="o">+</span><span class="s2">&quot;?dl=1&quot;</span><span class="p">)</span> <span class="c1"># note the last character as a string to request the file itself</span>
+<span class="n">wget</span><span class="o">.</span><span class="n">download</span><span class="p">(</span><span class="s2">&quot;https://www.dropbox.com/s/j5lxhd8uxrtsxko/&quot;</span><span class="o">+</span><span class="nb">str</span><span class="p">(</span><span class="n">fname</span><span class="p">)</span><span class="o">+</span><span class="s2">&quot;?dl=1&quot;</span><span class="p">)</span> <span class="c1"># note the last character as a string to request the file itself</span>
 </pre></div>
 </div>
 </div>

_sources/Chapter1-GettingStarted/1.1_open_reproducible_science.md

@@ -1,5 +1,5 @@
 # 1.1 Open Reproducible Science
 
-[![Lecture Slides](../img/Google_Slides_Logo.svg)](https://docs.google.com/presentation/d/1SCrx65-Q_CB8JRMN81Uk-QniPvPE-wozRD7YDDQEapc/edit?usp=sharing)
+[![Lecture Slides](../../img/Google_Slides_Logo.svg)](https://docs.google.com/presentation/d/1SCrx65-Q_CB8JRMN81Uk-QniPvPE-wozRD7YDDQEapc/edit?usp=sharing)
 
-[![YouTube video](../img/YouTube-Logo.wine.svg)](https://youtu.be/WeZ2vJxBuTg?si=aG1IuBNhCQnb1ljv)
+[![YouTube video](../../img/YouTube-Logo.wine.svg)](https://youtu.be/WeZ2vJxBuTg?si=aG1IuBNhCQnb1ljv)

_sources/Chapter2-DataManipulation/2.2_data_formats_rendered.ipynb

@@ -101,7 +101,7 @@
    "source": [
     "# Dowload the data using wget.\n",
     "fname = 'HYP_50M_SR'\n",
-    "wget.download(\"https://www.dropbox.com/s/r75ecms0bvyqaca/\"+str(fname)+\"?dl=1\") # note the last character as a string to request the file itself\n"
+    "wget.download(\"https://www.dropbox.com/s/j5lxhd8uxrtsxko/\"+str(fname)+\"?dl=1\") # note the last character as a string to request the file itself\n"
    ]
   },
   {

_sources/about_this_book/about_this_book.md

@@ -2,9 +2,11 @@
 
 The **GeoS**cience **MA**chine Learning **R**esources and **T**raining (GeoSMART) framework provides an educational pathway and a foundation in open source scientific ecosystems and progresses through general ML theory, toolkits, and deployment on Cloud computing.
 
-This book is used in the course offered at the University of Washington: Machine Learning in the Geoscienes (AUTMN 2022 - ESS 490C/590C). The corresponding GitHub repository with notebooks for the tutorials and homeworks is [MLGeo](https://github.com/UW-ESS-DS/MLGeo-Autumn22). Find the Docker image for the corresponding jupyter hub [MLGeo Image](https://github.com/UW-ESS-DS/MLGeo-image).
+This book is used in the course offered at the University of Washington: Machine Learning in the Geoscienes (AUTMN 2023 - ESS 469/569). The corresponding GitHub repository with notebooks for the tutorials and homeworks is [MLGeo](https://github.com/UW-ESS-DS/MLGeo-Autumn22). Find the Docker image for the corresponding jupyter hub [MLGeo Image](https://github.com/UW-ESS-DS/MLGeo-image).
 
-Instructor: Marine Denolle ([email protected])
+Instructors: 
+- Marine Denolle ([email protected])
+- Akshay Mehra ([email protected])
 Supported by: the GeoSMART team (Stefan Todoran, Nicoleta Cristea, Anthony Arendt, Scott Henderson, Ziheng Sun)
 
 # Overview

about_this_book/about_this_book.html

@@ -679,9 +679,13 @@ <h2> Contents </h2>
   <section class="tex2jax_ignore mathjax_ignore" id="machine-learning-in-the-geosciences">
 <h1>Machine Learning in the Geosciences<a class="headerlink" href="#machine-learning-in-the-geosciences" title="Permalink to this headline">#</a></h1>
 <p>The <strong>GeoS</strong>cience <strong>MA</strong>chine Learning <strong>R</strong>esources and <strong>T</strong>raining (GeoSMART) framework provides an educational pathway and a foundation in open source scientific ecosystems and progresses through general ML theory, toolkits, and deployment on Cloud computing.</p>
-<p>This book is used in the course offered at the University of Washington: Machine Learning in the Geoscienes (AUTMN 2022 - ESS 490C/590C). The corresponding GitHub repository with notebooks for the tutorials and homeworks is <a class="reference external" href="https://github.com/UW-ESS-DS/MLGeo-Autumn22">MLGeo</a>. Find the Docker image for the corresponding jupyter hub <a class="reference external" href="https://github.com/UW-ESS-DS/MLGeo-image">MLGeo Image</a>.</p>
-<p>Instructor: Marine Denolle (<a class="reference external" href="mailto:mdenolle&#37;&#52;&#48;uw&#46;edu">mdenolle<span>&#64;</span>uw<span>&#46;</span>edu</a>)
-Supported by: the GeoSMART team (Stefan Todoran, Nicoleta Cristea, Anthony Arendt, Scott Henderson, Ziheng Sun)</p>
+<p>This book is used in the course offered at the University of Washington: Machine Learning in the Geoscienes (AUTMN 2023 - ESS 469/569). The corresponding GitHub repository with notebooks for the tutorials and homeworks is <a class="reference external" href="https://github.com/UW-ESS-DS/MLGeo-Autumn22">MLGeo</a>. Find the Docker image for the corresponding jupyter hub <a class="reference external" href="https://github.com/UW-ESS-DS/MLGeo-image">MLGeo Image</a>.</p>
+<p>Instructors:</p>
+<ul class="simple">
+<li><p>Marine Denolle (<a class="reference external" href="mailto:mdenolle&#37;&#52;&#48;uw&#46;edu">mdenolle<span>&#64;</span>uw<span>&#46;</span>edu</a>)</p></li>
+<li><p>Akshay Mehra (<a class="reference external" href="mailto:akmehra&#37;&#52;&#48;uw&#46;edu">akmehra<span>&#64;</span>uw<span>&#46;</span>edu</a>)
+Supported by: the GeoSMART team (Stefan Todoran, Nicoleta Cristea, Anthony Arendt, Scott Henderson, Ziheng Sun)</p></li>
+</ul>
 </section>
 <section class="tex2jax_ignore mathjax_ignore" id="overview">
 <h1>Overview<a class="headerlink" href="#overview" title="Permalink to this headline">#</a></h1>