## Stirling Numbers, Including Negative Arguments

ModelingStatisticsposted by John Cook June 20, 2018

Stirling numbers are something like binomial coefficients. They come in two varieties, imaginatively called the first kind and second kind. Unfortunately it is the second kind that are simpler to describe and that come up more often in applications, so we’ll start there. Stirling numbers of... Read more

## Why NLP is a Great First AI Solution for Businesses

ModelingNLP & LLMsposted by Alex Amari June 19, 2018

The world of the C-suite isn’t quite ready to embrace artificial intelligence just yet. That’s according to an oft-cited Accenture study from late 2016, which found that less than half of the 1,700 business leaders interviewed in a global sample would feel comfortable trusting the advice... Read more

## A Different Use of Time Series to Identify Seasonal Customers

ModelingPredictive AnalyticsResearchposted by Kristen Kehrer June 18, 2018

I had previously written about creatively leveraging your data using segmentation to learn about a customer base.  The article is here.  In the article I mentioned utilizing any data that might be relevant.  Trying to identify customers with seasonal usage patterns was one of the variables that I mentioned... Read more

## Fixed Points of Logistic Function

ModelingStatisticsposted by John Cook June 15, 2018

Here’s an interesting problem that came out of a logistic regression application. The input variable was between 0 and 1, and someone asked when and where the logistic transformation f(x) = 1/(1 + exp(a + bx)) has a fixed point, i.e. f(x) = x. So given logistic regression parameters a and b, when does... Read more

## Missing the Point About Microservices – It’s About Testing and Deploying Independently

Modelingposted by Erik Bernhardsson June 13, 2018

Ok, so I have to first preface this whole blog post by a few things: I really struggle with the term microservices. I can’t put my finger on exactly why. Maybe because the term is hopelessly ill-defined, maybe because it’s gotten picked up by the hype train.... Read more

## Relative Error in the Central Limit Theorem

ModelingStatisticsposted by John Cook June 12, 2018

If you average a large number independent versions of the same random variable, the central limit theorem says the average will be approximately normal. That is the absolute error in approximating the density of the average by the density of a normal random variable will be small. (Terms... Read more

## Why are Convnets Often Better Than the Rest? Part I

Deep LearningModelingposted by Caspar Wylie, ODSC June 11, 2018

Introduction In this series, I will explore convolutional neural networks in comparison to standard neural networks. To begin with, the former is an evolution of the latter. Through analyzing this evolution, it is fascinating to see how particular design differences have such a great impact on... Read more

## Introduction to Machine Learning for Non-Developers

ModelingPredictive Analyticsposted by Pablo Casas June 7, 2018

About Machine Learning We all know that machine learning is about handling data, but it also can be seen as: The art of finding order in data by browsing its inner information. Some background on predictive models There are several types of predictive models. These models... Read more

## Quantifying Uncertainty with Bayesian Statistics

ModelingStatisticsposted by Mat Leonard June 5, 2018

Whenever we’re working with data, there is necessarily uncertainty in our results. Firstly, we can’t collect all the possible data, so instead we randomly sample from a population. Accordingly, there is a natural variance and uncertainty in any data we collect. There is also uncertainty from... Read more

## Reel Reviews: Neural Networks for Sentiment Analysis

Deep LearningModelingposted by Win Suen June 5, 2018

This is a joint article authored in collaboration between Kannan Sankaran and Win Suen. The Problem Over the past few years, there has been burgeoning interest in neural networks from data science and engineering communities. The advent of ever larger datasets, efficient commodity hardware, and powerful... Read more