No efforts will go to waste

I worked so so hard this January. I remember trying to re-watch the lecture on adversarial machine learning after work on the train back to Lausanne at 8:20pm, falling asleep and waking up way past my stop. I just started laughing in the train, laughing at myself for what I signed up for, for how absurdly tired I was.

So I finished my last exam on Saturday morning, rented a car Sat afternoon, packed up everything Sat night, drove to Geneva Sunday morning, unpacked, moved in, and returned the car. Monday I started full-time working at 9.

Today it’s the first Friday night, and I am so happy with where I am right now. Three years ago I was still debating of going to medical school or doing computer science. Today I was using Random Forest, XGBoost and GRU (ML methods) to build ML models. As I was writing scripts, assembling pipelines, debugging code, I felt powerful.

My new job turns out to be quite challenging, on par with projects at EPFL. There is a STRONG flavor of research to it – there is no ending point, no ready procedures to follow, no clear way of how to reach the target. It’s stressful because I am not sure if I can get satisfying results. It’s awesome because it’s intellectually engaging.

But I am mostly inspired to write this post because again, today is one of those days that really shows me – “No efforts will go to waste”. During the Machine Learning course this year, I made an extra effort to really write PyTorch code myself, to use the libraries, even if it means doing more than my share. And today, it paid off. I wouldn’t have known what GRU or LSTM are if not for my efforts, and without knowing I wouldn’t have thought of implementing them.