“Technical debt” is a term used in software development to describe delayed maintenance costs caused by initial tradeoffs between quality and speed. It’s a common metaphor on engineering teams as it makes the sometimes opaque costs of technical decisions easy to understand across the team. Technical debt functions just like
We are currently experiencing the negative impacts of the novel Coronavirus. This virus has quickly changed our lives and has left many of us feeling confused and fearful of what’s to come. As engineers and data scientists – we want to help make sense of the overwhelming amount of data
Despite being a global catastrophe, the outbreak should be an eye-opening experience to reshuffle our priorities.
Oftentimes, we are surprised by the accuracy of recommendations on what to buy on Amazon, watch on Netflix, or listen on Spotify. We feel that somehow these companies know how our brain works and monetizing this magical guessing game. They have a deep foundation on behavioral sciences, and our job
It probably goes without saying that building an app is far from easy. Time, cost, and quality are all key parts of the equation. Here at Rootstrap, we specialize in creative app development – we’ve worked with some of the biggest names in helping them develop ideas to apps. We
You could compare the speed and innovation of the mobile app industry to that of a world-class factory line. In the blink of an eye, a new app, technology, and a device have been created on a never-ending belt of new ideas. Off they go to be consumed but to
What is a Mobile App Development Platform Sometimes, it’s important to go back to the basics, especially if it’s your first go at mobile development. RMAD, open-source, hybrid apps – what do half of these things even mean? Let’s start at arguably the most important term you need to know:
What Is a Mobile App Development Tool These days, all you ever hear about is mobile apps. The consumer world has been there, done that with standard websites and online offerings. The new competitive standard for every business in almost every industry centers around a great app with a clean
Decision making is an art as old as time itself. Every single day, 7.44 billion human beings are faced with choices to make. From questions so small as whether to turn left or right to reach a destination to selections as colossal as choosing a lifetime partner, or picking the
We can make predictions with machine learning by generalizing our data’s pertinent characteristics. Summarizing diverse datasets provides insight that can help produce more relevant generalizations.
Data predictions provide probabilities of future outcomes by mining and analyzing existing data, also called training data. Effective prediction is a mix of engineering, statistics, and intuition. Summarization can help by shaping this intuition. In the generalization phase, we test our training data against new data, called test data, to calculate if our model is good enough to be used in real life. These two processes simplify large multidimensional datasets, so machine learning predictions can be applied to them. This article describes how summarization leads to generalization and then prediction through a real estate example.
