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    Five video classification methods

    Five video classification methods

    Code that accompanies my blog post outlining five video classification

    ...This reduces model complexity, training time, and a whole whack load of hyperparameters we don’t have to worry about. Every video will be subsampled down to 40 frames. So a 41-frame video and a 500-frame video will both be reduced to 40 frames, with the 500-frame video essentially being fast-forwarded. We won’t do much preprocessing. A common preprocessing step for video classification is subtracting the mean, but we’ll keep the frames pretty raw from start to finish.
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    OpenPoplog is a multi-language, incrementally-compiled virtual machine-based development environment with its own extensive teaching and support material, supporting the languages Pop-11, Prolog, Common Lisp and ML on Unix, VMS, Linux and Windows.
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    darknocK detects edges in images. It is using Artificial Neural Networks to enchance the results of standard algorithms.
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    Blackboard implements a lightweight, portable tuple space suitable for multi-agent system and distributed component design. Supports implicit invocation via content-filtered asynchronous events, blocking call semantics, and shared memory messaging.
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