or subscribe with
Join 0+ readers for one email each week.
Digests » 77
this week's favorite
Within the field of computer vision, facial recognition is an area of research and development which deals with giving machines the ability to recognize and verify human faces. Researchers primarily work on creating face recognition technology that can improve businesses and better human lives. To help strengthen your understanding of the technology, this guide will explain what facial recognition is, how it works, its various applications, and how accurate it is today.
What do we want to optimize for? Most of the businesses fail to answer this simple question. Every business problem is a little different, and it should be optimized differently. We all have created classification models. A lot of time we try to increase evaluate our models on accuracy. But do we really want accuracy as a metric of our model performance?
In areas like the Caribbean that face considerable risk from natural hazards like earthquakes, hurricanes, and floods, these forces of nature can have a devastating effect. This is especially true where houses and buildings are not up to modern construction standards, often in poor and informal settlements. While buildings can be retrofit to better prepare them for disaster, the traditional method for identifying high-risk buildings involves going door to door by foot, taking weeks if not months and costing millions of dollars.
We are releasing Spleeter to help the research community in Music Information Retrieval (MIR) leverage the power of a state-of-the-art source separation algorithm. It comes in the form of a Python Library based on Tensorflow, with pretrained models for 2, 4 and 5 stems separation. Spleeter will be presented and live-demoed at the 2019 ISMIR conference in Delft.
In this post, we’ll get into the weeds with some of the fundamentals of reinforcement learning. Hopefully, this will serve as a thorough overview of the basics for someone who is curious and doesn’t want to invest a significant amount of time into learning all of the math and theory behind the basics of reinforcement learning.