Learning

on Real-World Problems is Hard Reinforcement learning looks straightforward in controlled settings: well-defined states, dense rewards, stationary dynamics, unlimited simulation. Most benchmark results are produced under those assumptions. Observations are partial and noisy, rewards...

Introduction within the period of 2017-2019, physics-informed neural networks (PINNs) have been a very talked-about area of research within the scientific machine learning (SciML) community . PINNs are used to unravel atypical and partial...

, whether you’re a manager, an information scientist, an engineer, or a product owner, you’ve almost definitely been in no less than one meeting where the discussion revolved around “putting a model in production.” But...

within the federated learning series I'm doing, and should you just landed here, I might recommend going through the primary part where we discussed how federated learning works at a high level. For...

. What a present to society that is. If not for google trends, how would we've ever known that more Disney movies released within the 2000s led to fewer divorces within the UK. Or that drinking...

missed but hugely vital a part of enabling machine learning and subsequently AI to operate. Generative AI corporations are scouring the world for more data continuously because this raw material is required in...

I the concept of federated learning (FL) through a comic by Google in 2019. It was a superb piece and did a fantastic job at explaining how products can improve without sending user...

. Compliance wants fairness. The business wants accuracy. At a small scale, you'll be able to’t have all three. At enterprise scale, something surprising happens. Disclaimer:  The Regulator’s Paradox You’re a credit risk manager at a mid-sized...