In the days of online learning and remote work, students are gaining valuable skills to help them navigate their education. With new needs for greater independence in their learning journeys, better time management and finding communities in unexpected places, students are already encountering important lessons. Student-centered events like the Wolfram Emerging Leaders Program help foster success in the workplace and in life, no matter what happens in the world.
Date Archive: 2021 January
Editor’s note: The following post is based on the 2020 Advent of Code challenge, “an Advent calendar of small programming puzzles for a variety of skill sets and skill levels that can be solved in any programming language you like.” Guest author Philip Maymin delves into why he chose to use the Wolfram Language to solve these queries with a how-to on learning the language.
The past year of learning ushered in a variety of new experiences for instructors and students alike, and the United States Military Academy at West Point was no exception. In addition to masks in the classroom, reduced class sizes to allow for social distancing, rigorous testing and tracing efforts, and precautionary remote video classes, we have also needed to adjust aspects of our teaching styles. While such adjustments were voluntary, to enhance the discussion I chose to teach several lessons outside under large white tents and even in stadium bleachers to safely enable larger conversations with my cadets. Sometimes this meant carrying a large whiteboard with a tripod out to the stadium. At other times it meant putting quiz-style questions on a website so that students could submit answers via forms that were easier to grade while allowing everyone to work at a safe distance on individual devices.
Today’s handheld devices are powerful enough to run neural networks locally without the need for a cloud server connection, which can be a great convenience when you’re on the go. Deploying and running a custom neural network on your phone or tablet is not straightforward, though, and the process depends on the operating system of the machine. In this post, I will focus on iOS devices and walk you through all the necessary steps to train a custom image classifier neural network model using the Wolfram Language, export it through ONNX (new in Version 12.2), convert it to Core ML (Apple’s machine learning framework for iOS apps) and finally deploy it to your iPhone or iPad.