Tensors & Machine Learning

When it comes to building neural network models, there’s a lot of factors to consider such as hyperparameter tuning, model architecture, whether to use a pre-trained model or not, and so on and so forth. While it’s true that these are all important aspects to consider, I would argue that proper understanding of data representations […]

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Predicting Suicide Rates Using Linear Regression

Hi everyone! I’m taking an online deep learning with PyTorch course, which has turned out to be a really enjoyable experience. That being said, for our second assignment, our core focus was on building a Linear Regression model that predicts insurance charges. I sure finished that assignment. However, to spice things up a bit, I […]

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Exploring 5 PyTorch Functions

Hi everyone! I recently discovered a free, live-streamed 6-week PyTorch deep learning course on YouTube and decided to commit to it in the spare time that I have. The following is a copy of the Jupyter Notebook instance I wrote for the first assignment. This notebook can also be found on GitHub here. I hope […]

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NFL Team Ranking Approximation From 1970 – Current

Yesterday was the last day of my semester and a great day to put one of my favorite models, the Massey Method, from linear algebra to good use. As I revisited my linear algebra book to look back at all that we’ve learned through out the semester, the idea of finding out how NFL teams […]

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Launching Terp Data

It’s only been three days since I released Terp Data, a Google Chrome extension that allows University of Maryland students to view professor reviews and ratings on Testudo’s course pages instead of having to open up a third-party site to search for them. Since then however, many people have asked me what it took to […]

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