As I discussed in my last post, certain industries in the U.S. draw a younger (or older) demographic than others. There are a variety of reasons that younger people are more likely to work in a shoe store than a funeral home, but I’m not going to touch on that here. Instead, in this post I want to provide a more detailed view of the U.S. Bureau of Labor Statistics employment data. In the chart below, I plotted employment in the 13 high-level industries tracked by the BLS as a function of age groups ranging from 16-19 to 65+.

Ever wondered what industries employ the youngest and oldest people? Wonder no more. To create the visualization below, I pulled the employment data from the U.S. Bureau of Labor Statistics web site to rank all of the industries by the median age of the people working in them.

  || ||“The money that money earns, earns money”|

The outcome of the election for the IMLS (which runs ICML) adds Emma Brunskill and Hugo Larochelle to the board. The current members of the board (and the reason for board membership) are:

Polyaxon’s mission is to make deep learning simple and accessible to enterprises.

This note addresses the typical applied problem of estimating from data how a target “conversion rate” function varies with some available scalar score function — e.g., estimating conversion rates from some marketing campaign as a function of a targeting model score. The idea centers around estimating the integral of the rate function; differentiating this gives the rate function. The method is a variation on a standard technique for estimating pdfs via fits to empirical cdfs.

Intro

Hello Stitch Fix followers, check out what our fellow Stitch Fixers are doing in the month of March this year.

Here is the Answer:

In this latest blog, I’m responding to a cry for help. Someone got in touch with us recently asking for some advice on image detection algorithms, so let’s see what we can do!