Heatmapping is a simple and efficient way to analyze visitor interaction and user behavior on your website. If you are in a Conversion Rate Optimization (aka. CRO) project with your e-commerce or startup (or any other online) business, it’s indispensable to run some website heatmaps – such as click, mouse movement or scroll heatmaps.
In my last post I did some drawings based on L-Systems. These drawings are done sequentially. At any step, the state of the drawing can be described by the position (coordinates) and the orientation of the pencil. In that case I only used two kind of operators: drawing a straight line and turning a constant angle.
Crowding is a visual effect suffered by humans, in which an object that can be recognized in isolation can no longer be recognized when other objects, called flankers, are placed close to it. In this work, we study the effect of crowding in artificial Deep Neural Networks for object recognition. We analyze both standard deep convolutional neural networks (DCNNs) as well as a new version of DCNNs which is 1) multi-scale and 2) with size of the convolution filters change depending on the eccentricity wrt to the center of fixation. Such networks, that we call eccentricity-dependent, are a computational model of the feedforward path of the primate visual cortex. Our results reveal that the eccentricity-dependent model, trained on target objects in isolation, can recognize such targets in the presence of flankers, if the targets are near the center of the image, whereas DCNNs cannot. Also, for all tested networks, when trained on targets in isolation, we find that recognition accuracy of the networks decreases the closer the flankers are to the target and the more flankers there are. We find that visual similarity between the target and flankers also plays a role and that pooling in early layers of the network leads to more crowding. Additionally, we show that incorporating the flankers into the images of the training set does not improve performance with crowding.
Predictive maintenance is widely considered to be the obvious next step for any business with high-capital assets: harness machine learning to control rising equipment maintenance costs and pave the way for self maintenance through artificial intelligence (AI).
What makes BI tools great? What features are important while selecting a good BI tool? Let’s have a look. NYU MS in Business Analytics 2017NYU MS in Business Analytics
If you use an API key to access a secure service, or need to use a password to access a protected database, you’ll need to provide these ‘secrets’ in your R code somewhere. That’s easy to do if you just include those keys as strings in your code — but it’s not very secure. This means your private keys and passwords are stored in plain-text on your hard drive, and if you email your script they’re available to anyone who can intercept that email. It’s also really easy to inadvertently include those keys in a public repo if you use Github or similar code-sharing services. To address this problem, Gábor Csárdi and Andrie de Vries created the secret package for R. The secret package integrates with OpenSSH, providing R functions that allow you to create a vault to keys on your local machine, define trusted users who can access those keys, and then include encrypted keys in R scripts or packages that can only be decrypted by you or by people you trust.
How to take into account and how to compare information from different information sources? Multiple Factor Analysis is a principal Component Methods that deals with datasets that contain quantitative and/or categorical variables that are structured by groups. Here is a course with videos that present the method named Multiple Factor Analysis.
How to analyse of categorical data? Here is a course with videos that present Multiple Correspondence Analysis in a French way. The most well-known use of Multiple Correspondence Analysis is: surveys. Four videos present a course on MCA, highlighting the way to interpret the data. Then you will find videos presenting the way to implement MCA in FactoMineR, to deal with missing values in MCA thanks to the package missMDA and lastly a video to draw interactive graphs with Factoshiny. And finally you will see that the new package FactoInvestigate allows you to obtain automatically an interpretation of your MCA results. With this course, you will be stand-alone to perform and interpret results obtain with MCA.