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

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.

Miguel González-Fierro explains how to train a state-of-the-art deep neural network, ResNet, using Microsoft RSever and MXNet with the ImageNet dataset. (While most of the deep learning libraries are programmed in C++ and Python, only MXNet offers an API for R programmers.) Miguel then demonstrates how to operationalize this training for real-world business problems related to image classification.