We consider a population, partitioned into a set of communities, and study the problem of identifying the largest community within the population via sequential, random sampling of individuals. There are multiple sampling domains, referred to as \emph{boxes}, which also partition the population. Each box may consist of individuals of different communities, and each community may in turn be spread across multiple boxes. The learning agent can, at any time, sample (with replacement) a random individual from any chosen box; when this is done, the agent learns the community the sampled individual belongs to, and also whether or not this individual has been sampled before. The goal of the agent is to minimize the probability of mis-identifying the largest community in a \emph{fixed budget} setting, by optimizing both the sampling strategy as well as the decision rule. We propose and analyse novel algorithms for this problem, and also establish information theoretic lower bounds on the probability of error under any algorithm. In several cases of interest, the exponential decay rates of the probability of error under our algorithms are shown to be optimal up to constant factors. The proposed algorithms are further validated via simulations on real-world datasets.

Developer communities are one of the best ways to get updated with all the newest technologies and tools in the tech space. With the rise in demand for machine learning and data science among the organisations, enthusiasts are either choosing their ML journey from scratch or switching their roles. Joining the machine learning communities, one can have several benefits, including sharing tools and support, answering queries, mentorship, code reviews and much more. In this article, we list down 7 best machine learning developers communities an ML enthusiast must join. About: With a total of 1,070,309 members, the Reddit Machine Learning Community is one of the largest communities that is meant for industry professionals and is focused on practical aspects of building artificial intelligence systems.

"Kaggle is going to maintain independent brand for a while... what Kaggle has contributed to the community is democratisation of data, developer community... partnership with Kaggle is going to be very positive for us," said Chief Scientist Google Cloud (artificial intelligence and machine learning) Fei Fei Li here. Founded in 2010, Kaggle is home to the world's largest community of data scientists and machine-learning enthusiasts. More than 8 lakh data experts use Kaggle to explore, analyse and understand the latest updates in machine learning and data analytics. "We are providing them best technology to take advantage of Google Cloud, libraries etc," she said. "We must lower the barriers of entry to AI (artificial intelligence) and make it available to the largest community of developers, users and enterprises so that they can apply it to their own unique needs. With Kaggle joining the Google Cloud team, we can accelerate this mission," she pointed out.