Results for: "machine-learning"
Article by Gorilla Logic
Oct 2, 2019 AI,AWS IoT,Azure IoT,Big Data,connected enterprise,Internet of things,IoT,IoT solutions,machine learning,qa
For many businesses, IoT has rapidly become a powerful “weapon of mass disruption.” Regardless of whether your goal is to simply track data and control devices, or you aim to combine IoT with big data, artificial intelligence (AI), and machine learning to create a... Read more.
Article by Danis Matiaz
Oct 17, 2018 AR,augmented reality,AWS,CoreML,Docker,facial recognition app,Gunicorn,ios,machine learning,python,Turi Create
Building the Back End In this second post on how to build a face recognition app in iOS, we are going to focus on building the server and all the logic necessary to create the machine learning model and communicate with the app. One of the most common problems in... Read more.
Article by Fabricio Rodríguez
Aug 6, 2018 AR,augmented reality,AWS,CoreML,Docker,face recognition app,facial recognition,facial recognition app,Gunicorn,machine learning,python,Turi Create,Ubuntu
Augmented reality (AR) and machine learning are the hottest technologies on the market right now, so what we are going to build this time is a face recognition app that identifies people in our office and shows basic information when the app identifies someone who has... Read more.
Article by Ed Schwarz
Apr 24, 2018 Alpha Go,CPUs,Eclipse Foundation,machine learning,reinforcement learning,RL4J
I’ve been trying to teach my computer to play Gin Rummy. Actually, it’s a simplified version of rummy that uses 7-card hands to simply race to “gin”. For the rules, see below. My hope is to learn more about machine learning, a long-time... Read more.
Article by Kyle Balogh
Jul 6, 2017 api,Core ML,Core ML Integration,CoreML,dev tutorial,iOS11,machine learning,Not Hotdog,Silicon Valley,Swift,WWDC,WWDC17
This WWDC series highlights new technologies and insights I took away from WWDC 2017. My second post explored new drag and drop API. In this post, I demonstrate how easy it is to use the new Core ML framework. The Silicon Valley “not hotdog” reference was used... Read more.