It was a much needed break. I was burnt out working at Qualcomm during the first two years of my career, and to be honest I don't think I was a good fit for my team as I lacked understanding of hardware. I took a three month break to interview with startups and big-tech companies, got an offer from Amazon for 44% more pay and travelled to Turkey. I am returning to work in two weeks with a fresh mind.
Maybe I will start my own entrepreneurial journey in my late-20's but for now I am young.
Since reading code is significantly harder than writing code, I hope OpenAI's model allows users to infer a natural language explanation of a given snippet of code. This should be possible if it is a generative model.
Location: Toronto, ON, Canada
Remote: NO
Willing to relocate: Only to cities in Canada.
Technologies: Python, Deep Learning, numpy, scipy, pandas, C,C++, embedded C.
Résumé/CV: https://bit.ly/2Vi3Oy4
Email: [email protected]
I have prior experience with deep learning, accelerating deep neural architectures and statistics. I will graduate with my bachelors degree in May 2019.
I think the meetup protocol with this app is getting it wrong by asking women to initiate. Men tend to initiate despite the rejection of interest; most women are not as forward and find it attractive that the man approach them. Why would the app depend on women to initiate?
As an intern nearly done with work in the Bay Area I find the whole idea of permanently immigrating to Silicon Valley repugnant. Housing prices are in the millions, terrible healthcare, earthquakes every other week, dire state public transportation, apathetic attitude towards its homeless and, lots of groupthink. I suggest any YUPPIE (in a developed nation) with offers from a big four here reconsider their local offices imho. It is depressing to think this is the best place for STEM careers in the world.
Despite the controversy surrounding "debiasing" classifier outputs, I think further research in this area is still of merit. This area of research would help us understand and build transformations over latent / high level representation space, a general use case applicable to all fields interacting with machine learning.