ctandre

4 ms·

1. 1.▲
   by ctandre 7y ago

   Location: Los Angeles, CA Remote: No Willing to relocate: Yes Technologies: Python (w/ numpy, pandas, flask), C, C++, git Email: chrisandre01@gmail.com Résumé: https://drive.google.com/open?id=1d39L5Q-__F7kOzprtBcNrZYuY
2. 2.▲
   by ctandre 10y ago

   Ah yes - that sounds like the stochastic gradient descent I've been hearing about. That makes a lot of sense for very expensive models. Thanks for the response nshm - I've recently taken an interest in ML (coming in with some fami
3. 3.▲
   by ctandre 10y ago

   Do you mean to say that it is possible to design your parameters over all inputs without gradient descent? I'm somewhat confused, as I think that that would not be possible in the general case (e.g. nonlinear problems are hard to crack