roadmap

So without further ado, here are the posts I plan to make: Exotic python data structures and where to find them (heapq, OrderedDict, Deque, bisect) Onsite interview with Microsoft Reparameterization tricks: Gaussian vs gumbel The REINFORCE gradient estimator Probability notations Research itinerary Birthday post ABD, L1, and Deep learning Symposium follow-up Scale AI follow-up The supremum IS big-O notation (usually we want the TIGHTEST worst case bound) Model vs Data Parallelism [Some more reinforcement learning][1] [Some more reinforcement learning]1 (Note that the markdown in preview does not exactly work with some more exotic features, like citations and references) [Read More]