Most CS undergraduate mathematics stops just short of proof-based courses, which is a shame.
A proof-based linear algebra course would be the next course that is typical in the progression. If you've already done that, then an introductory real analysis course.
If you want something a bit more practical then maybe a probabilistic modeling course? ( think Gaussian mixture models, bayesian networks, plate models).
Location: NYC/Nomadic
Remote: I am only looking for remote roles.
Willing to relocate: No
Technologies: pytorch, BERT, general machine learning / nlp, quantization, pruning, weak supervision
Résumé/CV: https://drive.google.com/file/d/1WvpCoqU_3d7nRvzOJLAeULmh9bmIfpoQ/view
Email: jcjessecai@gmail.com
I am a machine learning researcher/engineer with a wide range of experiences. Most recently I worked on architecture optimization techniques for pytorch at Meta. Before that I was the first ML hire at a couple different companies: a series A NLP startup (Cultivate) that was acquired and at a mortgage scale-up (Blend) that eventually IPOd.
I really enjoy flat fast teams with smart and kind people. I have experience leading teams and managing people but would prefer a more technical role. I travel a lot and live a semi-nomadic lifestyle, so it's important to me to work somewhere that handles asynchronous work well. Open to contract / advisor work as well.
A proof-based linear algebra course would be the next course that is typical in the progression. If you've already done that, then an introductory real analysis course.
If you want something a bit more practical then maybe a probabilistic modeling course? ( think Gaussian mixture models, bayesian networks, plate models).