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    3. Mastering computer vision problems with state-of-the-art deep learning architectures, MXNet, and GPU virtual machines

    Mastering computer vision problems with state-of-the-art deep learning architectures, MXNet, and GPU virtual machines

    Posted By: naag
    Date: 10 Jul 2018 00:59:30
    Mastering computer vision problems with state-of-the-art deep learning architectures, MXNet, and GPU virtual machines

    Mastering computer vision problems with state-of-the-art deep learning architectures, MXNet, and GPU virtual machines
    MP4 | Video: AVC 1280x720 | Audio: AAC 44KHz 2ch | Duration: 38M | 391 MB
    Genre: eLearning | Language: English

    Deep learning has been especially successful in computer-vision tasks such as image classification because convolutional neural nets (CNNs) can create hierarchical levels of representations in an image. One of the most remarkable advances is ResNet, the CNN that surpassed human-level accuracy for the first time in history.

    ImageNet competition has become the de facto benchmark for image classification in the research community. The “small” ImageNet data contains more than 1.2 million images distributed in 1,000 classes.

    Mastering computer vision problems with state-of-the-art deep learning architectures, MXNet, and GPU virtual machines


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