Got your attention, right? Statistics are being used more strategically these days to convince readers to act or believe in certain ideas because they drive decisions in our personal and business lives. This strategy thrives in today’s headline-driven world as readers are drawn to eye-popping statistics and form their opinions accordingly. But because of how simple it is to develop a headline and how rarely the methods used to calculate statistics are questioned, it’s one of the easiest ways we’re seeing misinformation being spread about critical issues in our community. Almost anyone with a computer can access data and publish his/her opinions, which creates more opportunity to spread misinformation, whether intentional or not. …
Engineering neural networks from scratch with Python
Back in 2018, I first watched The Coding Train’s series on constructing neural networks from scratch with javascript. I felt it was pretty cool and have since wanted to make my own hacky, from-scratch implementation with python.
As I go along, I’ll attempt to explain my thought process using layman’s terms and as little math as possible.
# exponential function
from math import exp
# a very simple iteration operation
from itertools import tee
# library for n-dimensional arrays... I had made my own classes for vectors and matrices but, without hardware-accelerated operations for dot products and transposition, they relied on very slow loops that proved to be pretty impractical. …Exploring the mathematics and methodology behind the K-Nearest Neighbors Algorithm, Traditional Train/Test Split, and Repeated K-Fold Cross Validation
This article contains in-depth algorithm overviews of the K-Nearest Neighbors algorithm (Classification and Regression) as well as the following Model Validation techniques: Traditional Train/Test Split and Repeated K-Fold Cross Validation. The algorithm overviews include detailed descriptions of the methodologies and mathematics that occur internally with accompanying concrete examples. Also included are custom, fully functional/flexible frameworks of the above algorithms built from scratch using primarily NumPy. …
As the world continues to fight to contain the spread of COVID-19, our team at the Boulevard Consulting Group has, like millions of others, been working remotely since mid-March and staying at home in an effort to practice social distancing. As might have been the case for you, more time at home has meant plenty of time to binge-watch. Although we tend to watch shows as an escape from current events and responsibilities — quite successfully with Netflix’s “Tiger King” — we couldn’t help but find provoking parallels between our current world and the science-fiction realities portrayed in some of our favorite new television series and classic movies. …
Arlington, VA — February 14, 2020 — Today, roughly 20 percent of all relationships and 17 percent of all marriages begin online.[1] With about 2,000 dating apps operating in the US and almost 8,000 total operating worldwide, online dating has quickly become a multi-billion-dollar industry with most projections predicting at least $3 billion in revenue in the US alone for 2020.[2][3] Over the years, companies within the digital space have sought to remain relevant by integrating new technology to push the boundaries on efficiency — the online dating industry is no different. One subset of technologies that has proven indispensable for these issues is Artificial Intelligence (AI) and Machine Learning (ML). Both have quickly become no stranger to dating apps around the world. Nowadays, apps like Badoo, Betterhalf, eHarmony, OkCupid, Hinge, Tinder, and Match.com …
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