Machine Learning

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  • Machine Learning Software Market 2019| Global Trends, Regional Growth, Industry Analysis by 2026 | Research Industry US – News Obtain
    on November 13, 2019 at 9:44 am

    Machine Learning Software Market 2019| Global Trends, Regional Growth, Industry Analysis by 2026 | Research Industry US  News Obtain

  • [P] What is the best way to read data in batches from a datastore?
    by /u/MrDoOO on November 13, 2019 at 7:48 am

    I’m training a PyTorch model on data stored in Google BigQuery (basically a SQL like database). What’s the best way to fetch data in batches for training a model such that I don’t bottleneck the training process. Querying too many times is slow and the dataset is way too large to fit into memory. Any best practices here or existing tools for this? submitted by /u/MrDoOO [link] [comments]

  • Derbycon2019, Justin Leapline’s & Rick Yocum’s ‘Rise Of The Machines / Using Machine Learning With GRC’ – Security Boulevard
    on November 12, 2019 at 11:51 pm

    Derbycon2019, Justin Leapline’s & Rick Yocum’s ‘Rise Of The Machines / Using Machine Learning With GRC’  Security Boulevard

  • ABCs of UEBA: M is for Machine Learning – Security Boulevard
    on November 12, 2019 at 11:51 pm

    ABCs of UEBA: M is for Machine Learning  Security Boulevard

  • [D] Neural Differential Equations
    by /u/bthi on November 12, 2019 at 11:32 pm

    I had a question about these. I know that you calculate the whole network in one go and then you just evaluate it at some points along the the depth. However, I was wondering how the parameters work. 1) How are the weights and biases updated? I know they are “shared” through the whole network (and hence less parameters than the usual network) however, how do the individual evaluations work then? For the network. Like say the network is defined from t = 0 to t = 5, and I evaluate at t = 1 and t = 2; are the weights the same here and the only thing that changes is t? And if so, what’s the point even? Why not evaluate just at the end point (i.e. the maximum depth you want) ? 2) Going off of that, what is the point of those in between evaluations if the parameters are shared anyway? Wouldn’t they be updated the same way every time? Or is it that.. multiple evaluations means that the derivatives and the updates are “better”? I’m just really confused about this whole shared parameters thing. Please help! submitted by /u/bthi [link] [comments]