How to Apply Deep Learning to Real-World Problems

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Description

Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence.

Join Jennifer Marsman as she welcomes Sonja Knoll to the show as they take a deep dive into Deep Learning as well as apply some real-world scenarios for you to try out on your own.

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The Discussion

  • User profile image
    ppolyzos

    The hyperlink tag for cognitive services is not closed properly, so the link is broken as it points to https://www.microsoft.com/cognitive-servicesBuild, which is not valid.

    Correct link for Cognitive Services is https://www.microsoft.com/cognitive-services

  • User profile image
    gduncan411

    @ppolyzos: Thanks for the heads-up... fixed

  • User profile image
    gduncan411

    @ppolyzos: Thanks for the heads-up, it's been fixed

  • User profile image
    Jules

    Sorta awesome stuff.
    Just a shame that the return to legacy neural networks methods, is such a slow learner technique, requiring powerful CPU, clouds for competitive deep machine learning.

  • User profile image
    Tim

    have you tried to apply the word completion approach to number recognition? What would be the impact on the whole deep learning approach if the input data set is processed in real-time- the pixels of a picture for example are being introduced to the processor in random way?

  • User profile image
    Sonja

    Hi Tim - No, I haven’t tried that. To be clear, are you thinking of images as "sequences" of pixels? (similar to sequences of words for the word completion task). If that’s the case, I suppose one could use some sequence-related algorithms, like RNN/LSTM, but with two dimensions. Typically, CNNs are used for images since they encode the proximity of neighboring pixels. To your second point, one could submit the image to the model before it’s fully loaded, and get less-than-optimal results until the image is fully loaded.

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