In recent years, as commercial capital nourishing technology development, many technologies that challenge humanity are subverting human society: internet giant monopoly, DNA editing, artificial intelligence, etc. One of the core reason that made this “technological explosion” possible is the well-developed computer science industry, which revolutionized the inheritance of knowledge for human beings. More and more STEM elites are dedicating their whole life to the contemporary technological explosion, hoping to promote humanity to a whole new level. The rapid technological development naturally reminds me of my favorite science fiction “The Three-Body Problem”, where I first learned the concept of technological explosion:
Tags: machine learningThu, Apr 25, 2019
Recommender systems are one of the most prominent examples of machine learning in the wild today. They determine what shows up in your Facebook news feed, in what order products appear on Amazon, what videos are suggested in your Netflix queue, as well as countless other examples. But what are recommender systems, and how do they work? This post is the first in a series exploring some common techniques for building recommender systems as well as their implementation.
Tags: recommenderThu, Apr 11, 2019
Algebraic geometry is not a subject that often arises in conversations around data science and machine learning. However, recent work in the field of tropical geometry (a subset of algebraic geometry) suggests that this subject might be able give some insights into the types of functions representable by neural networks (as well as give some upper bounds on the complexity of functions representable by neural nets of fixed width and depth).
Tags: algebraic geometry