Skip to content
View AlfaBetaBeta's full-sized avatar

Highlights

  • Pro

Block or report AlfaBetaBeta

Block user

Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users.

You must be logged in to block users.

Please don't include any personal information such as legal names or email addresses. Maximum 100 characters, markdown supported. This note will be visible to only you.
Report abuse

Contact GitHub support about this user’s behavior. Learn more about reporting abuse.

Report abuse
AlfaBetaBeta/README.md

Introduction

I am a computational structural modeller turned data scientist.

If the origin seems less intuitive than the destination, I don’t blame you. It is not the most common journey. In essence, though, and regardless of the disciplines it connects, it is a journey above everything else. Etymologically I would even say an Odyssey, with a nonlinear Troy populated by finite elements and Ithaca being the elusive kingdom of data. It is an Odyssey because it embeds the notion of return to the longed home, nostalgia for the the place that gives purpose.

Does this resonate with you? Let me try to condense this journey into an image.

Granted, attempting to plot an allegory has limitations. The learning journey is meant to be endless whereas the arch is finite (it couldn’t be otherwise, it is literally made of finite elements, check out this repository if you are keen on generating it yourself). Also, I could not find compelling interpretations of the arch width or its thickness. Speaking of the curse of narrative dimensionality. But you get the idea.

And what substantiates the journey, what makes the arch stable? The common mindset across disciplines. The same notional pipeline leading to insights from raw data.

So, why did I embark on this journey? Because similar landscapes may look familiar, but only one feels like home.

Contact

You can always open an issue in a repository of your interest. However, for general comments or suggestions, feel free to reach out through these channels:

AlfaBetaBeta | LinkedIn AlfaBetaBeta | Email


Skills

Here is a sample of my fields of interest, with a showcasing example per tool:

Python Jupyter ScikitLearn TensorFlow Flask R SQL MongoDB Gmsh GitHub



Miscellanea

Aside from learning, I like sharing the notions I have assimilated once they fully make sense to me (nothing makes you learn better than teaching). A little side project I have kept rolling is a series of video animations à la 3b1b about structural analysis and design. It is still under construction (involuntary pun) but once the first couple of videos are ripe, I will flesh out the repository here and start uploading those to a YouTube channel, hopefully not too far down the line. Here is a teaser if this topic tickles your curiosity:

Incidentally, if you found the introduction evocative, I elaborated more on the notion of nostalgia and inspiration on this podcast. Embark on that journey at your own peril.

Pinned Loading

  1. gmsh-crack-generator gmsh-crack-generator Public

    gmsh plugin to generate cracks inside 3D FE solid meshes.

    Python 16 3

  2. Association-Rules Association-Rules Public

    Association rule mining and visualisation on a sample dataset of an online teaching platform.

    Jupyter Notebook

  3. gmsh-3D-arch-bridge gmsh-3D-arch-bridge Public

    Systematic generation of a 3D Finite Element macroscale mesh of a 3-span arch bridge via gmsh.

    GLSL 3

  4. DeepLearning-MLP-Tutorial DeepLearning-MLP-Tutorial Public

    Elaborations on TensorFlow tutorial on a MLP implementation.

    Jupyter Notebook

  5. Spark-Movie-Ratings Spark-Movie-Ratings Public

    This notebook performs EDA over a movie ratings dataset via pyspark sql.

    Jupyter Notebook 2

  6. Football-Network-Analysis Football-Network-Analysis Public

    Network analysis is performed on several European football leagues via R library igraph.

    R