Microsoft's plan to use machine learning to improve eyecare in India Competition that results in better care for people suffering from visual impairments is the right kind of competition. Following a path similar to that of Google's DeepMind, Microsoft India announced this morning that it's launching a new research group, the Microsoft Intelligent Network for Eyecare, to bring data-driven eyecare services to India. Whereas DeepMind's swing at ophthalmology targeted the UK, Microsoft's ambitions are a considerably more global. The tech company is working alongside researchers from the United States, Brazil, Australia and, of course, India to train machine learning models that can identify conditions that can lead to blindness. Microsoft's key strategic partnership is with the L V Prasad Eye Institute in Hyderabad, India, one of the most prestigious hospitals in the country.

Sorting cucumbers by shape, size, and how good they look, takes a lot of effort. And just like most repetitive tasks that require effort, can be automatised through Deep Learning! Find out how. "It's not hyperbole to say that use cases for machine learning and deep learning are only limited by our imaginations. About one year ago, a former embedded systems designer from the Japanese automobile industry named Makoto Koike started helping out at his parents' cucumber farm, and was amazed by the amount of work it takes to sort cucumbers by size, shape, color and other attributes. Makoto's father is very proud of his thorny cucumber, for instance, having dedicated his life to delivering fresh and crispy cucumbers, with many prickles still on them. Straight and thick cucumbers with a vivid color and lots of prickles are considered premium grade and command much higher prices on the market. But Makoto learned very quickly that sorting cucumbers is as hard and tricky as actually growing them. "Each cucumber has different color, shape, quality and freshness," Makoto says."

Competition that results in better care for people suffering from visual impairments is the right kind of competition. Following a path similar to that of Google's DeepMind, Microsoft India announced this morning that it's launching a new research group, the Microsoft Intelligent Network for Eyecare, to bring data-driven eyecare services to India. Whereas DeepMind's swing at ophthalmology targeted the UK, Microsoft's ambitions are a considerably more global. The tech company is working alongside researchers from the United States, Brazil, Australia and, of course, India to train machine learning models that can identify conditions that can lead to blindness. Microsoft's key strategic partnership is with the L V Prasad Eye Institute in Hyderabad, India, one of the most prestigious hospitals in the country.

This is the first installment in a three-part review of 2016 in machine learning and deep learning. In Part Two, we cover developments in each of the leading open source machine learning and deep learning projects. Part Three will review the machine learning and deep learning moves of commercial software vendors. As organizations expand the use of machine learning for profiling and automated decisions, there is growing concern about the potential for bias. In 2016, reports in the media documented racial bias in predictive models used for criminal sentencing, discriminatory pricing in automated auto insurance quotes, an image classifier that learned "whiteness" as an attribute of beauty, and hidden stereotypes in Google's word2vec algorithm.

And then, at the very top layer you have detectors that can tell you whether you're looking at a person or a dog or a sailplane or whatever it is." Next, let's imagine that we want to teach a computer what a cat looks like using deep learning. First, we'd take a neural network and program different layers to identify different elements of a cat: claws, paws, whiskers, etc. (Each layer would itself be built on layers that allow it to recognize that particular element, but that's why this is called deep learning.) Then, the network is shown a lot of images of cats and other animals and told which is which. "This is a cat," we tell the computer, showing it a picture of a cat. "This is also a cat.

Pulling these threads together, recently a gaming AI defeated a US Air Force expert combat pilot in simulated combat.

Whether by accident or design, the details of Google's plans for artificial intelligence (AI) have been elusive. In some cases, there's no real mystery, just nothing all that exciting to talk about. AI technology is the foundation of the company's search engine, and the most obvious reason for Google's high-profile, $400M acquisition of DeepMind in 2014 is to use the UK firm's expertise in deep learning--a subset of AI research, but more on that later--to bolster that core capability. But the Googleplex has absorbed other bright minds from the field of AI, as well as some of the most buzzed-about companies in robotics, with only some of that collective braintrust officially allocated to driverless cars, delivery drones or other publicly announced robotics or AI-related projects. What, exactly, are Google's AI experts up to?

Fantastic resource created by Andrea Motosi. I've only included the 5 categories that are the most relevant to our audience, though it has 31 categories total, including a few on distributed systems and Hadoop. Click here to view the 31 categories. You might also want to check our our our internal resources (the first section below). Train Convolutional Neural Networks (or ordinary ones) in your browser Decider: Flexible and Extensible Machine Learning in Ruby Etsy Conjecture: scalable Machine Learning in Scalding Google Sibyl: System for Large Scale Machine Learning at Google H2O: statistical, machine learning and math runtime for Hadoop MLbase: distributed machine learning libraries for the BDAS stack MLPNeuralNet: Fast multilayer perceptron neural network library for iOS and Mac OS X nupic: Numenta Platform for Intelligent Computing: a brain-inspired machine intelligence platform, and biologically accurate neural network based on cortical learning algorithms PredictionIO: machine learning server buit on Hadoop, Mahout and Cascading scikit-learn: scikit-learn: machine learning in Python Spark MLlib: a Spark implementation of some common machine learning (ML) functionality Sparkling Water: combine H2OÕs Machine Learning capabilities with the power of the Spark platform Vahara: Machine learning and natural language processing with Apache Pig Viv: global platform that enables developers to plug into and create an intelligent, conversational interface to anything Vowpal Wabbit: learning system sponsored by Microsoft and Yahoo!

Talk to someone with programming skills and discuss any subject about deep learning with them so that you could quickly jump in as a newbie. Though some people figure out various libraries embedding math is used universally, you needn't understand the theory to implement deep learning tasks, I still recommend you learn some math knowledge like partial derivative. Some resources could give you a good starting point like Stanford's online course CS231n, Deep Learning at Oxford 2015and Andrew Ng's Coursera class. Also, some interesting online books like Neural Networks and Deep Learning could also give you an assistance to deep learning. Facilities and toolkits should also be available.