This short post is a call to arms for Tsetlin Machines (TMs), a somewhat academic outsider’s view of what the community has and what is missing. The discourse is biased to commercial usages of the technology, a vital step forward in establishing the true value of the algorithms discussed in this blog post.. Introduction 🔗Tsetlin Machines are a new class of classifier that have significant usability and performance benefits over both standard classifiers, and most interestingly over existing deep learning (DL) systems.
Hi All! I recently delivered a talk about polars at PyDataLondon23. Here’s the slides data and notebook. Hope you find them useful! This data has ben created as an open data set by CitizenMe as part of their 360° Data Lab initiative.
The ability to perform accurate repetitive computation has been central to a large number of scientific and technological advances in the last seventy years. At the heart of this commercially is Moore’s Law, which states that computational power provided by traditional computer central processing units will double year-on-year for the foreseeable future. Unfortunately, several factors have compounded to make this less likely to continue. Heat dissipation, atomic and quantum effects provide practical limits to the miniaturisation and packing of transistors, and the limited bandwidth between CPU and memory limits computation speed.