A place to put links and pictures of projects I want potential employers to see.
At the current time, my suggestion is to use the Google CoLab Versions of the Jupyter Notebooks (or the PDF versions) which will show input and output of each cell of code.
If you have more time, it might even be better to look at the MyBinder Version of the Jupyter Notebooks, since they are more interactive and it's easier to keep the versions of programs compatible. It does take longer, because a Docker image is getting created and served.
In additional to some fun projects with general programming, I'm going to put in some of my favorite visualizations and Machine Learning work. Any of this stuff could be completely-guided-from-tutorials or completely-original, or anywhere in between.
I'll just use links here, with each link pointing to a markdown file in the
porftfolio-resume repo with. Each of these markdown files will have with a
short description of the project and any visualization, some images with
screencaps, and a link to the main repo; it will be a presentation page. Here,
I'll be making a list of the projects with links to each presentation page I
described above. There will also be a link to each actual repo with the
full project. Since I leave the notebooks without output, so that you can run
through them yourself, I'm going to add two waysFinally, if I get things all
figured out for them to work, I'll put a link to an online, interactive Jupyter
Notebook made available thanks to the MyBinder project.
Suggestion for Now: If you want to be able to check things out quickly, use the CoLab specific notebook, which you can
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Can we get the data from the CMS experiment (part of the LHC) and (re-)discover the Higgs Boson - also called the God Particle? You can judge for yourself. The figure from my analysis is at top left. Two different views of the discovery plot shown in the discovery publication are below and to the right of my plot.
Higgs Boson Discovery Presentation Page
higgs_boson_visualisation repo
[To put on the presentation page] This was my chance to return to grad school, where I studied Nuclear/Particle Physics. The goal was to do the data analysis to "re-discover" the Higgs Boson - also called the God Particle. I got the data, prepared it, and ran the analysis to create a histogram that looks pretty similar to the publicatin's discovery figure. Below is a sneak peak at my work and the discovery figure.
Suggestion for Now: If you want to be able to check things out quickly, use the CoLab specific notebook, which you can
This is part 4, which is the most interesting. If you have the time and the inclination, part 3 is the next one I'd suggest. All parts are linked below. Note that here, as with the other Google CoLab Notebooks, the point is to give a notebook with the input and the output. For some, you might be able to re-do the code running. For others, the versions of Python and the libraries won't be compatible. You can see the MyBinder versions for those.
Note that you can't jump from one part to another inside of the CoLab notebooks. You'll need
to keep coming back to this README to click on the CoLab links (the part 1, part 2, etc.
links above.
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This is a nice project that contains some fundamental parts of Natural Language Processing. I use it for my job applications. Currently, I'm thinking about running it for a job that really interests me, using AI/ML and Data Science to help in the medical-imaging business, but I'm not sure if the potential employer would like to have the job description out where everyone can find it.
Check Match between Job Description and Job Application Presentation Page (not yet set up)
Suggestion for Now: If you want to be able to check things out quickly, use the CoLab specific notebook, which you can
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This is a better answer to a job interview question that I felt I didn't answer very well. It's a nice combination of NLP and Machine Learning basics.
CNN for NLP in PyTorch Presentation Page (not yet set up)
nlp_w_pytorch_zhongyu-pan repo
[To put on the presentation page] This is my favorite, nice, and simple code for a simple CNN, created using PyTorch. I really like how I did the first part - using the (Transformer-type/LLM) AI to do each of the steps described by the teacher in the course this project follows. That course is Natural Language Processing with PyTorch, taugh by Zhongyou Pan. Note that the previous link is to a LinkedIn course, and thus will require a login, which itself will require an account.
Can we read the numbers in zip codes that people write on their Post-Office envelopes? The MNIST digit database and challenge give us the chance to find out.
Presentation Page (not yet set up)
@TODO: I'll need to include information about how to set up the directories used in the project in the CoLab Runtime machine.
CoLab Version with Single-Hidden-Layer Approach
CoLab Version with CNN Approach
Some of the most exciting finds in Manuscript Studies are previously unknown books in the binding of other books. Important finds - works previously believed to be lost as well as untapped genealogical data - come as researchers look at what book-binders called waste and used to bind and protect other works. We bring Machine Learning and Artificial Intelligence into the search.
[Images cropped on interesting parts.]
These images are details of interesting finds in the dataset I've been building and using.
manuscript-waste-reuse-finder repo
Suggestion for Now: If you want to be able to check things out quickly, use the CoLab specific notebook, which you can