Deep Learning has made several breakthroughs in recent years. Compared to traditional computation platforms, it has become more sophisticated and advanced than ever. Smart homes, intelligent personal assistant, etc. are some of the major breakthroughs in the present era. In this article, we list down 8 platforms which can be used to build mobile deep learning solutions. Facebook's open-source deep learning framework, Caffe2 is a lightweight, modular, and scalable framework which provides an easy way to experiment with deep learning models and algorithms. The framework comes with native Python and C APIs that work interchangeably and integrates with Android Studio, Microsoft Visual Studio, or XCode for mobile development.

Deep Learning has been getting more and more traction. It focuses on one section of Machine Learning: Artificial Neural Networks. This article explains why Deep Learning is a game-changer in analytics, when to use Deep Learning, and how visual analytics allows business analysts to leverage the analytic models built by a (citizen) data scientist. Deep Learning is the modern buzzword for Artificial Neural Networks, one of many concepts in Machine Learning that is used to build analytics models. A neural network works similarly to a human brain.

In conjunction with the Web Summit conference in Lisbon today, Facebook is unveiling artificial intelligence (AI) software that it's using in order to let users apply and switch artistic styles for live video streams on Android and iOS. After demonstrating the technology at a conference last month, Facebook is now testing the video style transfer technology on mobile in a few countries, and it will be deployed more widely in the near future. The Caffe2go technology Facebook developed in the past three months is an implementation of a hot type of AI called deep learning, which typically involves training on lots of data, like images, and then making inferences about new data. In this case, Facebook has developed pre-trained neural networks that can then make inferences about new data on the fly on mobile. Google did something similar with a part of Google Translate last year, but Google also recently demonstrated neural style transfer technology of its own, although it's not yet been shown to run on mobile devices.

Earlier this week, Google made a splash when it released its TensorFlow artificial intelligence software on GitHub under an open-source license. Google has a sizable stable of AI talent, and AI is working behind the scenes in popular products, including Gmail and Google search, so AI tools from Google are a big deal. Today on GitHub, TensorFlow, primarily written in C, is the top trending project of the day, the week, and the month, having accrued more than 10,000 stars in about one week. But there are several other open-source tools to choose from on GitHub if you want to improve your app with deep learning, a type of AI that involves training artificial neural networks on a bunch of data and then getting them to make inferences about new data. There are other frameworks available today -- these are just the most interesting ones I've encountered -- and more will surely emerge in the future.