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
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
