![]() However, for many complex applications, such as drug discovery, recommender systems, and of course, wireless networks, we would benefit from a bit less structure. ![]() Since many ML models, like convolutional neural networks, rely heavily on linear algebra, we often prefer matrix-like data structures. Some inputs, like images and financial time series, lend themselves to well-defined representations, like arrays, vectors, and tensors. Often, the way we choose represent a dataset is as important as the data itself. Here are some example API calls that I implemented with the requests library in Python: Open-source ledgers provide fault-tolerance and auditability, but they are also fantastic datasets for data mining. Dataset CurationĪll the data you will see here was generated by making requests to the publicly-available Helium Blockchain API. I’d encourage you to read more in Helium’s documentation. This constant communication between nearby hotspots helps prove that adequate coverage is provided in a given geographic region, and miners are rewarded for providing this service in an honest way. Any hotspot that “witnesses” (receives) this transmission reports it to the blockchain in a receipt. ![]() Essentially, each wireless radio in the network periodically broadcasts “beacons”, which are just small packets of encrypted data. This article is going to assume that you have some basic familiarity with the Helium Blockchain and specifically, its purpose-built work algorithm called Proof of Coverage. Photo by Alina Grubnyak on Unsplash Quick Primer on the Helium Network
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