Update Deep Learning With PyTorch landing page

_includes/deep_learning_form.html

@@ -1,69 +1,22 @@
-<div id="mc_embed_signup">
-  <form
-    action="https://twitter.us14.list-manage.com/subscribe/post?u=75419c71fe0a935e53dfa4a3f&id=91d0dccd39"
-    method="post"
-    id="mc-embedded-subscribe-form"
-    name="mc-embedded-subscribe-form"
-    class="email-subscribe-form validate deep-learning-form"
-    target="_blank">
-    <div id="mc_embed_signup_scroll" class="email-subscribe-form-fields-wrapper">
-      <div class="mc-field-group">
-        <label for="mce-EMAIL" style="display:none;">Email Address</label>
-        <input type="email" value="" name="EMAIL" class="required email deep-learning-input" id="mce-EMAIL" placeholder="Enter Your Email">
-      </div>
-
-      <div id="mce-responses" class="clear">
-        <div class="response" id="mce-error-response" style="display:none"></div>
-        <div class="response" id="mce-success-response" style="display:none"><a href="{{ site.baseurl}}/deep-learning-with-pytorch-thank-you" target="_blank"></a></div>
-      </div>    <!-- real people should not fill this in and expect good things - do not remove this or risk form bot signups-->
-
-      <div style="position: absolute; left: -5000px;" aria-hidden="true"><input type="text" name="b_75419c71fe0a935e53dfa4a3f_91d0dccd39" tabindex="-1" value=""></div>
-      <button id="deep-learning-button" class="btn btn-lg with-right-arrow module-button email-subscribe-button">Download Book</button>
-    </div>
-  </form>
+<div>
+  <iframe
+    class="col-12"
+    id="iframe-id"
+    src="https://docs.google.com/forms/d/e/1FAIpQLSd7gnpQggoetUnn2DvCy7teNn_35rDH9Xnlsr-4jkUJYHdEGg/viewform?embedded=true"
+    height="780"
+    frameborder="0"
+    marginheight="0"
+    marginwidth="0">
+    Loading…
+  </iframe>
 </div>
 
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-<script type='text/javascript'>
-  var bouncer = new Bouncer("#mc-embedded-subscribe-form", {
-  // Don't automatically submit the form if it's verified
-  disableSubmit: true
-  });
-  document.addEventListener(
-    "bouncerFormValid",
-    function(event) {
-      var $form = $(event.target);
-      var $button = $form.find(".email-subscribe-button");
-
-      $.ajax({
-        type: "GET",
-        url:
-          "https://twitter.us14.list-manage.com/subscribe/post-json?u=75419c71fe0a935e53dfa4a3f&amp;id=91d0dccd39&c=?",
-        data: $form.serialize(),
-        dataType: "json",
-        contentType: "application/json; charset=utf-8",
-        success: function(data) {
-          if (data.result == "success") {
-            window.location.href =
-              "{{site.baseurl}}/deep-learning-with-pytorch-thank-you";
-          } else {
-            if (data.msg.includes("is already subscribed to list")) {
-              window.location.href =
-                "{{site.baseurl}}/deep-learning-with-pytorch-thank-you";
-            } else {
-              $form
-                .find("#mce-error-response")
-                .html("There was an error submitting the form. Please try again.")
-                .show();
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-          }
-        },
-        error: function(e) {
-          window.location.href =
-            "https://twitter.us14.list-manage.com/subscribe/post-json?u=75419c71fe0a935e53dfa4a3f&amp;id=91d0dccd39&c=?";
-        }
-      });
-    },
-    false
-  );
+<script>
+  var load = 0;
+  $('#iframe-id').on('load', function() {
+    load++;
+    if (load > 1) {
+      window.location.href = "{{site.baseurl}}/deep-learning-with-pytorch-thank-you";
+    }
+  })
 </script>

_layouts/deep_learning.html

@@ -18,14 +18,11 @@
           <div class="col-md-7 deep-learning-jumbotron-text">
             <h1>Deep Learning <br> with PyTorch</h1>
             {% if page.deep-learning-landing == true %}
-              <p class="lead deep-learning-landing-text">Download a free copy of the Essential Excerpts from the book and learn how to get started with AI / ML development using PyTorch</p>
+              <p class="lead deep-learning-landing-text">Download a free copy of the full book and learn how to get started with AI / ML development using PyTorch</p>
             {% else %}
               <p class="lead deep-learning-thank-you-text">Thanks for requesting a copy of the Deep Learning with PyTorch book!
                 <span class="download-book-link"><a href="/assets/deep-learning/Deep-Learning-with-PyTorch.pdf" target="_blank">Click here</a> to download the book.</span>
               </p>
-              <p class="lead deep-learning-landing-text">
-                Visit the <a href="https://www.manning.com/books/deep-learning-with-pytorch" target="_blank">Manning Early Access Program</a> for the latest chapters.
-              </p>
             {% endif %}
           </div>
           <div class="col-md-5 deep-learning-book-container {% if page.deep-learning-landing == false %}thank-you-book-container{% endif %}">

_sass/deep-learning.scss

@@ -21,6 +21,10 @@
     .deep-learning-jumbotron-text {
       @include desktop {
         margin-top: 55px;
+
+        h1 {
+          padding-top: 30px;
+        }
       }
       @include small-desktop {
         max-width: 95%;
@@ -146,10 +150,7 @@
 
 .deep-learning-book {
   max-width: 100%;
-  height: 200px;
-  @include desktop {
-    height: auto;
-  }
+  height: 400px;
 }
 
 .deep-learning-form {

deep-learning/deep-learning-index.html

@@ -9,13 +9,16 @@
 
 <div class="row">
   <div class="col-md-12 deep-learning-col">
-    <p>Deep Learning with PyTorch provides a detailed, hands-on introduction to building and training neural networks with PyTorch, a popular open source machine learning framework. This Essential Excerpts edition includes:</p>
+    <p>Deep Learning with PyTorch provides a detailed, hands-on introduction to building and training neural networks with PyTorch, a popular open source machine learning framework. This full book includes:</p>
     <ul>
       <li>Introduction to deep learning and the PyTorch library</li>
       <li>Pre-trained networks</li>
       <li>Tensors</li>
       <li>The mechanics of learning</li>
       <li>Using a neural network to fit data</li>
+      <li>Using convolutions to generalize</li>
+      <li>Real-world examples: Building a neural network designed for cancer detection</li>
+      <li>Deploying to production</li>
     </ul>
     <p>Get a free copy for a limited time.</p>
   </div>