American Sign Language (ASL) is the language used by the deaf community in the United States and Canada; and uses hand gestures, or a series of hand gestures, to communicate information. It is estimated that approximately 500,000 people use ASL as their primary language 1 . To expand access to the deaf community, it is necessary for us to develop tools to facilitate communication between the deaf and hearing communities. For this project, I explored the possibility of using a neural network to recognize hand signs in images. Findings This project was conducted using a Kaggle dataset containing approximately 35,000 images of 24 hand signs. Each hand sign represented a different letter of the English alphabet. The letters "J" and "Z" were excluded because their signs require motion and cannot be captured in a single image. The image below shows the hand sign for the letter "C". A Convolutional Neural Network (CNN) was created to extract featu
If you follow National Geographic on Instagram ( @natgeo ), then you are well aware they post multitudes of pictures every day depicting everything from animals to people to nature and more. But what do their followers really want to see? After analyzing user engagement, I learned that National Geographic is most successful when they post about animals; and is not-so-successful when they share images of people. The following analysis describes how to measure and predict user engagement, and provides insights into how these results can be used by brands to increase engagement on social media. Findings The 500 most recent posts to @natgeo's Instagram were analyzed to determine what their followers are most interested in seeing. Each image was run through a visual recognition software to obtain a list of labels describing the contents of each picture. Below you can see an example post from @natgeo's Instagram and the associated caption and im age labels. Caption: Photo