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  • Deepfakes Expose Societal Dangers of AI, Machine Learning

    Deepfakes Expose Societal Dangers of AI, Machine Learning

    Deepfake videos are enabled by machine learning and data analytics, and at best can be a form of entertainment. However, at worst, deepfake videos can have dangerous effects on our society. Here's why.

    Sourced through Scoop.it from: sea.pcmag.com

  • JavaScript and machine learning: Google shows what’s possible using the web programming language

    JavaScript and machine learning: Google shows what’s possible using the web programming language

    From head-controlled Pacman to browser-based upload filters -- here are some of the best uses of TensorFlow.js.

    Sourced through Scoop.it from: www.techrepublic.com

  • Understanding ML.NET And How .NET Developers & ML Enthusiasts Can Get Started

    Understanding ML.NET And How .NET Developers & ML Enthusiasts Can Get Started

    ML.NET is aimed at providing the end-end workflow for consuming ML into .NET apps across various steps of machine learning

    Sourced through Scoop.it from: www.analyticsindiamag.com

  • Machine learning algorithms explained

    Machine learning algorithms explained

    Machine learning uses algorithms to turn a data set into a predictive model. Which algorithm works best depends on the problem

    Sourced through Scoop.it from: www.infoworld.com

  • Why Google believes machine learning is its future

    Why Google believes machine learning is its future

    We heard so much about machine learning at Google I/O this year.

    Sourced through Scoop.it from: arstechnica.com

  • Google says it will address AI, machine learning model bias with technology called TCAV

    Google says it will address AI, machine learning model bias with technology called TCAV

    TCAV is short for Testing with Concept Activation Vectors and its a technology that can identify high concept signals and therefore defend against bias.

    Sourced through Scoop.it from: www.zdnet.com

  • How to tell whether machine-learning systems are robust enough for the real world

    How to tell whether machine-learning systems are robust enough for the real world

    MIT researchers have devised a method that detects inputs called “adversarial examples” that cause neural networks to misclassify inputs, to better measure how robust the models are for various real-world tasks.

    Sourced through Scoop.it from: news.mit.edu

  • Machine learning algorithms explained

    Machine learning algorithms explained

    Machine learning uses algorithms to turn a data set into a predictive model. Which algorithm works best depends on the problem

    Sourced through Scoop.it from: www.infoworld.com

  • Top 5 skills needed to become a machine learning engineer

    Top 5 skills needed to become a machine learning engineer

    The demand for machine learning engineers continues to grow. Tom Merritt lists five skills necessary to land the job.

    Sourced through Scoop.it from: www.techrepublic.com

  • Microsoft makes a push to simplify machine learning –

    Microsoft makes a push to simplify machine learning –

    Ahead of its Build conference, Microsoft today released a slew of new machine learning products and tweaks to some of its existing services. These range from no-code tools to hosted notebooks, with a number of new APIs and other services in-between. The core theme, here, though, is that Microsoft is continuing its strategy of democratizing […]

    Sourced through Scoop.it from: techcrunch.com

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