WEST CHESTER, Pennsylvania — January 31, 2018 — Today, The Polaris Team released the Polaris February Update. The primary goal of this update is to improve the user experience of the administrators and weekend duty crews.
WEST CHESTER, Pennsylvania — January 31, 2018 — Today, Kevin Wang, developer of Argus and Count: A Very Simple Counter (Count), released Count 3.0 to the general public on the App Store. Originally released in 2015, Count is a simplistic and intelligent counter that allows users to count with gestures. The new Count has been completely rebuilt from the ground up and comes with new features including an improved UI, Sense, Random, and various bug fixes and stability improvements.
Usually, I begin my independent project with a blog post containing a detailed plan for the semester. This time, I feel compelled to write about an interesting experience with submitting my app to Apple’s App Store and getting it rejected twice by the App Review Board.
In my last blog post, I detailed the implementation of machine learning models in iOS applications using the Core ML and Vision frameworks. As you probably remember from the tutorial, I implemented the Inception v3 model to give the app the ability to classify 1,000 common objects in the world. While it is true that you can easily download the model from a Github repository, have you ever wonder where it came from? In this blog post, I will introduce the “brain” behind the Inception v3 model––an artificial neural network (ANN).
Recently, I have been experimenting with CoreML, the machine learning framework for Apple’s mobile and desktop operating systems. Rather than continue my discussion of linear regression, I will detail the implementation of a model with CoreML in this blog post.
You might remember linear regression from statistics as a method to produce a linear equation that models the relationship between two variables. Not surprisingly, linear regression is quite similar in machine learning, except that the focus is on the prediction rather than the interpretation of data. Regression is a supervised learning algorithm (if you remember from my previous blog) that predicts real-valued output when given an input. In this blog post, I will discuss the model representation of simple linear regression and introduce its cost function.
There are two widely accepted definitions of machine learning. The phrase is first coined in 1959 by computer scientist Arthur Lee Samuel, who trained a computer program to play checkers with humans. He later described his work as “the field of study that gives computers the ability to learn without being explicitly programmed.” Decades later, Professor Tom Mitchell coined a more modern and formal definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.”
Why machine learning? It all started back in August when I was getting Westtown Resort ready for this school year.
As I briefly mentioned in my previous blog posts, Resort utilizes a MySQL data table that resembles this one: