Deployed 2f6f967 with MkDocs version: 1.5.3

assets/graph.html

@@ -88,8 +88,8 @@ <h1></h1>
 
 
                   // parsing and collecting nodes and edges from the python
-                  nodes = new vis.DataSet([{"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "NLP Work", "label": "NLP Work", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "https://github.com/zaiquiriw/nlp-portfolio", "label": "https://github.com/zaiquiriw/nlp-portfolio", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "Summary_of_Attention_Article.pdf", "label": "Summary_of_Attention_Article.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "wordnet.pdf", "label": "wordnet.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "ngrams-assignment.pdf", "label": "ngrams-assignment.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "summary.pdf", "label": "summary.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "text-classification.pdf", "label": "text-classification.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "RickMortyTwo.pdf", "label": "RickMortyTwo.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "ML Work", "label": "ML Work", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "nlp overview", "label": "nlp overview", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "data exploration", "label": "data exploration", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "linear regression.pdf", "label": "linear regression.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "linear classification.pdf", "label": "linear classification.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "from scratch.pdf", "label": "from scratch.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "similarities main.pdf", "label": "similarities main.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "similarities regression.pdf", "label": "similarities regression.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "similarities classification.pdf", "label": "similarities classification.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "Clustering.pdf", "label": "Clustering.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "Dimensionality.pdf", "label": "Dimensionality.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "SVM and ensemble.pdf", "label": "SVM and ensemble.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "ClassificationSVM.pdf", "label": "ClassificationSVM.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "RegressionSVM.pdf", "label": "RegressionSVM.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "keras_image_recognition.pdf", "label": "keras_image_recognition.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "index", "label": "index", "shape": "dot", "size": 10}]);
-                  edges = new vis.DataSet([{"from": "NLP Work", "to": "https://github.com/zaiquiriw/nlp-portfolio", "width": 1}, {"from": "NLP Work", "to": "Summary_of_Attention_Article.pdf", "width": 1}, {"from": "NLP Work", "to": "wordnet.pdf", "width": 1}, {"from": "NLP Work", "to": "ngrams-assignment.pdf", "width": 1}, {"from": "NLP Work", "to": "summary.pdf", "width": 1}, {"from": "NLP Work", "to": "text-classification.pdf", "width": 1}, {"from": "NLP Work", "to": "RickMortyTwo.pdf", "width": 1}, {"from": "ML Work", "to": "nlp overview", "width": 1}, {"from": "ML Work", "to": "data exploration", "width": 1}, {"from": "ML Work", "to": "linear regression.pdf", "width": 1}, {"from": "ML Work", "to": "linear classification.pdf", "width": 1}, {"from": "ML Work", "to": "from scratch.pdf", "width": 1}, {"from": "ML Work", "to": "similarities main.pdf", "width": 1}, {"from": "ML Work", "to": "similarities regression.pdf", "width": 1}, {"from": "ML Work", "to": "similarities classification.pdf", "width": 1}, {"from": "ML Work", "to": "Clustering.pdf", "width": 1}, {"from": "ML Work", "to": "Dimensionality.pdf", "width": 1}, {"from": "ML Work", "to": "SVM and ensemble.pdf", "width": 1}, {"from": "ML Work", "to": "ClassificationSVM.pdf", "width": 1}, {"from": "ML Work", "to": "RegressionSVM.pdf", "width": 1}, {"from": "ML Work", "to": "keras_image_recognition.pdf", "width": 1}, {"from": "index", "to": "Summary_of_Attention_Article.pdf", "width": 1}, {"from": "index", "to": "keras_image_recognition.pdf", "width": 1}, {"from": "index", "to": "ML Work", "width": 1}, {"from": "index", "to": "NLP Work", "width": 1}]);
+                  nodes = new vis.DataSet([{"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "NLP Work", "label": "NLP Work", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "https://github.com/zaiquiriw/nlp-portfolio", "label": "https://github.com/zaiquiriw/nlp-portfolio", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "Summary_of_Attention_Article.pdf", "label": "Summary_of_Attention_Article.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "wordnet.pdf", "label": "wordnet.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "ngrams-assignment.pdf", "label": "ngrams-assignment.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "summary.pdf", "label": "summary.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "text-classification.pdf", "label": "text-classification.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "RickMortyTwo.pdf", "label": "RickMortyTwo.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "ML Work", "label": "ML Work", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "nlp overview", "label": "nlp overview", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "data exploration", "label": "data exploration", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "linear regression.pdf", "label": "linear regression.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "linear classification.pdf", "label": "linear classification.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "from scratch.pdf", "label": "from scratch.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "similarities main.pdf", "label": "similarities main.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "similarities regression.pdf", "label": "similarities regression.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "similarities classification.pdf", "label": "similarities classification.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "Clustering.pdf", "label": "Clustering.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "Dimensionality.pdf", "label": "Dimensionality.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "SVM and ensemble.pdf", "label": "SVM and ensemble.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "ClassificationSVM.pdf", "label": "ClassificationSVM.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "RegressionSVM.pdf", "label": "RegressionSVM.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "keras_image_recognition.pdf", "label": "keras_image_recognition.pdf", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "index", "label": "index", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "resume", "label": "resume", "shape": "dot", "size": 10}]);
+                  edges = new vis.DataSet([{"from": "NLP Work", "to": "https://github.com/zaiquiriw/nlp-portfolio", "width": 1}, {"from": "NLP Work", "to": "Summary_of_Attention_Article.pdf", "width": 1}, {"from": "NLP Work", "to": "wordnet.pdf", "width": 1}, {"from": "NLP Work", "to": "ngrams-assignment.pdf", "width": 1}, {"from": "NLP Work", "to": "summary.pdf", "width": 1}, {"from": "NLP Work", "to": "text-classification.pdf", "width": 1}, {"from": "NLP Work", "to": "RickMortyTwo.pdf", "width": 1}, {"from": "ML Work", "to": "nlp overview", "width": 1}, {"from": "ML Work", "to": "data exploration", "width": 1}, {"from": "ML Work", "to": "linear regression.pdf", "width": 1}, {"from": "ML Work", "to": "linear classification.pdf", "width": 1}, {"from": "ML Work", "to": "from scratch.pdf", "width": 1}, {"from": "ML Work", "to": "similarities main.pdf", "width": 1}, {"from": "ML Work", "to": "similarities regression.pdf", "width": 1}, {"from": "ML Work", "to": "similarities classification.pdf", "width": 1}, {"from": "ML Work", "to": "Clustering.pdf", "width": 1}, {"from": "ML Work", "to": "Dimensionality.pdf", "width": 1}, {"from": "ML Work", "to": "SVM and ensemble.pdf", "width": 1}, {"from": "ML Work", "to": "ClassificationSVM.pdf", "width": 1}, {"from": "ML Work", "to": "RegressionSVM.pdf", "width": 1}, {"from": "ML Work", "to": "keras_image_recognition.pdf", "width": 1}, {"from": "index", "to": "resume", "width": 1}, {"from": "index", "to": "Summary_of_Attention_Article.pdf", "width": 1}, {"from": "index", "to": "keras_image_recognition.pdf", "width": 1}, {"from": "index", "to": "ML Work", "width": 1}, {"from": "index", "to": "NLP Work", "width": 1}]);
 
                   nodeColors = {};
                   allNodes = nodes.get({ returnType: "Object" });

index.html

@@ -54,7 +54,7 @@
 <input autocomplete="off" class="md-toggle" data-md-toggle="search" id="__search" type="checkbox"/>
 <label class="md-overlay" for="__drawer"></label>
 <div data-md-component="skip">
-<a class="md-skip" href="#my-projects">
+<a class="md-skip" href="#tldr">
           Skip to content
         </a>
 </div>
@@ -197,10 +197,20 @@
     </label>
 <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix="">
 <li class="md-nav__item">
+<a class="md-nav__link" href="#tldr">
+    TLDR
+  </a>
+</li>
+<li class="md-nav__item">
 <a class="md-nav__link" href="#my-projects">
     My Projects
   </a>
 </li>
+<li class="md-nav__item">
+<a class="md-nav__link" href="#keep-up-with-zaiquiri">
+    Keep up with Zaiquiri
+  </a>
+</li>
 </ul>
 </nav>
 </li>
@@ -233,10 +243,20 @@
     </label>
 <ul class="md-nav__list" data-md-component="toc" data-md-scrollfix="">
 <li class="md-nav__item">
+<a class="md-nav__link" href="#tldr">
+    TLDR
+  </a>
+</li>
+<li class="md-nav__item">
 <a class="md-nav__link" href="#my-projects">
     My Projects
   </a>
 </li>
+<li class="md-nav__item">
+<a class="md-nav__link" href="#keep-up-with-zaiquiri">
+    Keep up with Zaiquiri
+  </a>
+</li>
 </ul>
 </nav>
 </div>
@@ -277,11 +297,15 @@ <h1>Howdy There!</h1>
 <p>My academic journey in Computer Science was transformative, but it was my focus on machine learning that truly shaped my passion and expertise. The hands-on experience I gained has fueled my desire to continue learning and growing in the field of data science.</p>
 <p>Influenced by my professor, who transitioned from industry to academia, I too am considering a similar path. I believe that real-world experience can provide invaluable insights that can be brought back into academic or research settings.</p>
 <p>Feel free to explore my projects and reach out if you have any questions or opportunities that align with my interests.</p>
+<h3 id="tldr">TLDR<a class="headerlink" href="#tldr" title="Permanent link">¶</a></h3>
+<p>If you want the quick version of my skills, just look over my short and sweet <a href="resume" title="resume-10-23.pdf">resume-10-23.pdf</a>.</p>
 <h3 id="my-projects">My Projects<a class="headerlink" href="#my-projects" title="Permanent link">¶</a></h3>
 <p>Out of my different projects, I’m most proud of the work I did researching <a href="Summary_of_Attention_Article.pdf" title="explainability in attention based models">explainability in attention based models</a> and fine tuning Google’s MobileNetV2 for <a href="keras_image_recognition.pdf" title="identifying grains of rice">identifying grains of rice</a>.</p>
 <p><a class="glightbox" data-desc-position="bottom" data-height="auto" data-type="image" data-width="100%" href="assets/img/Pasted%20image%2020231029141843.png"><img alt="Pasted image 20231029141843.png" src="assets/img/Pasted%20image%2020231029141843.png" title="Pasted image 20231029141843.png"/></a></p>
 <p>You can view all of my introduction to <a href="ML%20Work/" title="machine learning">machine learning</a> work as well as my <a href="NLP%20Work/" title="natural language processing">natural language processing</a> work as pdfs here, or look at the source on <a href="https://github.com/zaiquiriw">github</a>.</p>
 <p>I can’t wait to do more projects and add more experince to this site!</p>
+<h3 id="keep-up-with-zaiquiri">Keep up with Zaiquiri<a class="headerlink" href="#keep-up-with-zaiquiri" title="Permanent link">¶</a></h3>
+<p>This month I’ll be committing <a href="https://nanowrimo.org/national-novel-writing-month">nanowrimo</a> and begin work on a podcast where I talk through common programming problems and algorithms through the lense of science fiction and AI. I hope to not only improve as a critical thinker but help explain difficult concepts to other people as well. I’ll post my progress here in updates once I get started!</p>
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search/all_files.json

@@ -113,6 +113,12 @@
         "name": "report",
         "url": "report.pdf"
     },
+    {
+        "src_uri": "resume-10-23.pdf",
+        "dest_uri": "resume-10-23.pdf",
+        "name": "resume-10-23",
+        "url": "resume-10-23.pdf"
+    },
     {
         "src_uri": "sentence_parsing.pdf",
         "dest_uri": "sentence_parsing.pdf",