A Year of Machine Learning, Neural Networks, and more

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My goal this year, and with this project, is to read, replicate, and expand upon as many different papers, tutorials, and Github repositories as possible. Each week I plan on going through all the links and tabs I’ve collected over the months and posting whatever the results are here. In addition, I want to improve my writing skills and the only way to do that is by practice and repetition. Please forgive my writing the first few months, it will, it has to, get better.

It’s relatively easy (🤓) to take online classes, courses, and nanodegrees but getting your hands dirty and working through problems on your own is where real learning begins. I’m very aware that I’m probably not going to be leading authority in the field of artificial intelligence (or even someone worth a seven figure salary), that was a decision I should have made fifteen years ago. However, becoming proficient/efficient at implementing models and being up-to-date with the latest research is a huge step in the right direction.

In this first week of January I’m going to try to nail down the workflow that I use to actually complete this project and to document what I learn. I have a local folder ready for any code or papers that need to be stored locally and a NAS to store large datasets. Everything will be on Github for version control and to show/share my work (and so I can get more practice using Git). I’m going to be using my 2014 13” MacBook Pro for most of this project so there definitely won’t be any training time records set. In my experience, though, simple models run quickly and long training times may incentive me to upgrade my machine.

The pace at which deep learning and artificial intelligence advances are being made (no matter how specialized) is truly astounding. I, for one, welcome our new 🤖 overlords.

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