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Add resume and personal blurb
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zaiquiriw committed Oct 29, 2023
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Expand Up @@ -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": "index", "label": "index", "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": "keras_image_recognition.pdf", "label": "keras_image_recognition.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 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": "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": "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}]);
edges = new vis.DataSet([{"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}, {"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}]);
nodes = new vis.DataSet([{"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "index", "label": "index", "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": "keras_image_recognition.pdf", "label": "keras_image_recognition.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 Work", "label": "NLP Work", "shape": "dot", "size": 10}, {"color": "#97c2fc", "font": {"color": "#7c7c7c"}, "id": "resume-10-23.pdf", "label": "resume-10-23.pdf", "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": "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": "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}]);
edges = new vis.DataSet([{"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}, {"from": "index", "to": "resume-10-23.pdf", "width": 1}, {"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}]);

nodeColors = {};
allNodes = nodes.get({ returnType: "Object" });
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10 changes: 9 additions & 1 deletion docs/index.md
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Expand Up @@ -9,6 +9,9 @@ My academic journey in Computer Science was transformative, but it was my focus
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.

Feel free to explore my projects and reach out if you have any questions or opportunities that align with my interests.
## TLDR
If you want the quick version of my skills, just look over my short and sweet [[resume|resume-10-23.pdf]].

## My Projects

Out of my different projects, I'm most proud of the work I did researching [[Summary_of_Attention_Article.pdf|explainability in attention based models]] and fine tuning Google's MobileNetV2 for [[keras_image_recognition.pdf|identifying grains of rice]].
Expand All @@ -17,4 +20,9 @@ Out of my different projects, I'm most proud of the work I did researching [[Sum

You can view all of my introduction to [[ML Work.md|machine learning]] work as well as my [[NLP Work.md|natural language processing]] work as pdfs here, or look at the source on [github](https://github.com/zaiquiriw).

I can't wait to do more projects and add more experince to this site!
I can't wait to do more projects and add more experince to this site!

## Keep up with Zaiquiri
This month I'll be committing [nanowrimo](https://nanowrimo.org/national-novel-writing-month) 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!


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